{"paper_id":"3ba33f8a-a32f-410d-bfda-826fe0eef20b","body_text":"Multi-omics-guided metabolic engineering of Limosilactobacillus reuteri for high-level 3-hydroxypropionaldehyde production from glucose | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Multi-omics-guided metabolic engineering of Limosilactobacillus reuteri for high-level 3-hydroxypropionaldehyde production from glucose Zuojun Liu, Qiang Yin, Liang Li, Hang Wu, Yong Fang, Fuqiang Cheng, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9269523/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 9 You are reading this latest preprint version Abstract Background Limosilactobacillus reuteri is an important probiotic chassis for producing 3-hydroxypropionaldehyde (3-HPA), a broad-spectrum antimicrobial and biochemical. However, its production is constrained by a metabolic trilemma comprising coenzyme B₁₂ auxotrophy, redox imbalance, and carbon catabolite repression (CCR). The main bottleneck currently limiting cost-effective glucose-based production is the inability to simultaneously resolve these interconnected constraints. Results Through integrated multi-omics analyses of differential carbon source performance, an unexpected regulatory mechanism was obtained. Galactose orchestrates a tripartite metabolic program wherein UDP-galactose acts as a central signaling molecule that simultaneously alleviates CCR via Crc inhibition, redirects carbon flux through the pentose phosphate pathway to balance ATP/NADH, and transcriptionally co-activates dhaB (glycerol dehydratase) and cobA/Q (B₁₂ biosynthesis) for coordinated holoenzyme assembly. Strikingly, this native regulatory network enables 8.51 g/L 3-HPA from galactose despite its poor support for cell growth, versus 1.67 g/L from glucose which readily supports robust growth—a 5.1-fold advantage that highlights galactose's metabolic priming efficacy. Guided by these insights, an engineered strain overexpressing glycerol dehydratase was combined with process optimization and a rationally designed four-factor targeted supplementation. Synergistic supplementation with coenzyme B₁₂, ATP, KCl, and MgCl₂ achieved 28.96 g/L 3-HPA—a 17.34-fold improvement over native glucose-based performance. Conclusions To our knowledge, this is the first systems-level strategy for compensating the metabolic trilemma in L. reuteri by engineering carbon source-dependent regulatory networks and cofactor dynamics in a cost-effective glucose-based system, achieving the highest 3-HPA titer reported in lactobacilli in shake-flask fermentation to date. This work establishes a scalable platform for industrial 3-HPA production and provides a systems-level framework for reprogramming central carbon metabolism in probiotic chassis to address persistent bioproduction bottlenecks. Limosilactobacillus reuteri Metabolic Engineering Pathway Optimization 3-Hydroxypropionaldehyde Carbon source utilization Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Background As antimicrobial resistance increases, interest in sustainable alternatives to conventional antibiotics has intensified, and probiotic-derived antimicrobials have gained attention within “One Health” strategies. Limosilactobacillus reuteri , a commensal bacterium widely distributed in the gastrointestinal tracts of vertebrates, produces reuterin, a broad-spectrum antimicrobial agent comprising hydrated, non-hydrated, and dimeric forms of 3-hydroxypropionaldehyde (3-HPA). This compound exhibits activity against both Gram-positive and Gram-negative bacteria, fungi, and protozoa, providing L. reuteri with a competitive advantage in its ecological niches [ 1 ]. Beyond its antimicrobial properties, 3-HPA also holds considerable promise as a biochemical precursor for the synthesis of valuable compounds such as acrylic acid and functional polymers, highlighting its industrial potential [ 2 ]. Despite this considerable translational potential, scaling 3-HPA biosynthesis in L. reuteri remains difficult because of fundamental metabolic constraints that limit industrial viability. In this organism, 3-HPA production is intrinsically linked to glycerol metabolism, a pathway constrained by several interconnected limitations [ 3 – 5 ]. First, the process is strictly dependent on coenzyme B₁₂ because the catalytic activity of glycerol dehydratase (GDHt), the key enzyme in the pathway, requires this cofactor for optimal function [ 6 ]. However, L. reuteri possesses only limited endogenous capacity for B₁₂ synthesis, resulting in suboptimal enzyme activation [ 7 ]. Second, pronounced redox imbalance arises because NADH accumulates during glycerol metabolism, which promotes the diversion of 3-HPA toward 1,3-propanediol via NADH-dependent propanediol oxidoreductase (PDOR), thereby substantially reducing 3-HPA yield [ 8 ]. Third, carbon catabolite repression (CCR) mediated by global regulators such as Crc suppresses glycerol metabolic genes in the presence of preferred carbon sources such as glucose, drastically limiting carbon flux into the 3-HPA biosynthetic pathway [ 9 ]. These three limitations, coenzyme B₁₂ scarcity, redox stress, and transcriptional repression, collectively form a tightly interconnected “metabolic trilemma” that complicates engineering strategies for enhanced 3-HPA production. Current research specifically targeting 3-HPA enhancement in L. reuteri remains limited. Although one study used carboxyl-functionalized superparamagnetic nanoparticles attached to the bacterial surface to improve conversion efficiency, this strategy increases operational complexity and cost [ 10 ]. Carbon source selection represents an underexplored yet potentially transformative lever for metabolic optimization. Carbon sources are pivotal determinants of microbial metabolic profiles because they dictate the distribution of carbon flux across pathways [ 11 ]. Glucose, a preferred carbon source for most microorganisms, rapidly activates glycolysis but often triggers CCR, thereby suppressing genes involved in alternative metabolic routes [ 12 ]. Heterologous expression of 3-HPA biosynthesis pathways in Escherichia coli has also been attempted to circumvent native regulation in L. reuteri , but these approaches sacrifice the probiotic compatibility required for food and pharmaceutical applications [ 13 ]. Translating 3-HPA production to an industrially viable scale requires improvements in both titer and cost. In other microorganisms such as Klebsiella pneumoniae , metabolic engineering for 1,3-propanediol production has achieved notable success, validating key strategies including cofactor rebalancing and endogenous cofactor synthesis [ 14 ]. Zhang et al. engineered K. pneumoniae to efficiently convert glycerol to 1,3-propanediol by reprogramming central metabolism to minimize byproducts and autonomously supply critical cofactors, thereby eliminating the need for exogenous vitamin B₁₂ through enhanced endogenous synthesis [ 15 ]. These advances demonstrate the potential of systematic metabolic engineering. Similarly, the use of low-cost carbon sources such as glucose presents a compelling route for L. reuteri , but its potential to boost 3-HPA yield remains largely untapped because of CCR [ 16 ]. In this study, an integrated approach combining multi-omics technologies and metabolic engineering was used to decipher the carbon source-dependent metabolic trilemma in L. reuteri LR618. The metabolic responses to different carbon sources were systematically investigated, revealing that galactose orchestrates a tripartite mechanism: UDP-galactose accumulation alleviates Crc-mediated CCR, pentose phosphate pathway flux rebalances ATP/NADH pools, and transcriptional co-activation of dhaB (GDHt) and cob genes coordinates holoenzyme assembly. Guided by these mechanistic insights, a rationally designed engineering strategy was implemented in a glucose-based fermentation system by combining GDHt overexpression with targeted supplementation of coenzyme B₁₂, ATP, K⁺, and Mg²⁺. This approach achieved a 17.34-fold increase in 3-HPA yield (28.96 g/L) compared with the native glucose-based system, substantially exceeding previously reported levels. This work provides a systems-level framework for leveraging native metabolic networks in probiotic chassis and indicates that coordinated multi-target interventions are important for overcoming the metabolic trilemma in sustainable antimicrobial production. Materials and Methods Bacterial strain and cultivation conditions L. reuteri LR618, isolated from pig feed, was used as the experimental strain. To investigate the effects of different carbon sources on strain growth and subsequent 3-hydroxypropionaldehyde (3-HPA) production, pre-cultures were established in de Man-Rogosa-Sharpe (MRS) broth. The medium was supplemented with one of the following sole carbon sources: glucose, lactose, galactose, sucrose, dextrin, soluble starch, maltose, fructose, deoxyribose, xylose, or glycerol, each at 20 g/L, consistent with the carbohydrate concentration in the standard MRS formulation. Static cultures were incubated at 37°C under 5% CO₂ for 26 h. Growth was monitored by measuring the optical density at 600 nm ( OD ₆₀₀), and cultures were harvested at the stationary phase to ensure a consistent physiological state for subsequent experiments. Glycerol conversion reactions After 26 h of cultivation, cells were harvested by centrifugation at 8,000 × g for 10 min at 4°C. The pellets were washed twice with sterile phosphate-buffered saline (pH 6.0) and resuspended in glycerol conversion medium consisting of 50 mM potassium phosphate buffer (pH 6.0) supplemented with 600 mM glycerol. The final inoculum size was adjusted to a wet cell weight (WCW) of 10 g/L. Bioconversion reactions were carried out in 250 mL Erlenmeyer flasks at 37°C under static conditions to produce 3-HPA. Samples were collected every 0.5 h. A 5 mL aliquot of the reaction mixture was rapidly chilled on ice to halt metabolic activity and then centrifuged at 12,000 × g for 5 min at 4°C to separate cells from the supernatant. Supernatants were stored at 4°C for biochemical analyses to quantify 3-HPA and other metabolites. Cell pellets were frozen in liquid nitrogen and stored at − 80°C for metabolomic and transcriptomic profiling. The 3-HPA concentration was determined spectrophotometrically. A standard curve was established using purified 3-HPA (0.05–1.5 mg/L) dissolved in ultrapure water. For sample analysis, 1 mL culture supernatant was mixed with 0.75 mL 10 mM tryptophan reagent and 3 mL concentrated HCl, followed by vortexing and incubation at 37°C for 20 min. Absorbance at 560 nm was measured using a UV-Vis spectrophotometer (Shimadzu, Japan). The 3-HPA concentration was then calculated from the standard curve. Metabolomic profiling of LR618 Metabolite extraction from cells: A 100 µL aliquot of cell pellets was mixed with 400 µL extraction solution (MeOH:ACN, 1:1, v/v). The extraction solution contained deuterated internal standards. The mixture was vortexed for 30 s, sonicated for 10 min in a 4°C water bath, and incubated for 1 h at − 40°C to precipitate proteins. Samples were then centrifuged at 12,000 rpm for 15 min at 4°C. The supernatant was transferred to a fresh glass vial for analysis. The quality control sample was prepared by pooling equal aliquots of the supernatants from all samples. LC-MS analysis: LC-MS/MS analyses were performed using a UHPLC system (Vanquish, Thermo Fisher Scientific) equipped with a Waters ACQUITY UPLC BEH Amide column (2.1 mm × 50 mm, 1.7 µm) coupled to an Orbitrap Exploris 120 mass spectrometer (Thermo). The mobile phase consisted of 25 mmol/L ammonium acetate and 25 mmol/L ammonium hydroxide in water (pH = 9.75) (A) and acetonitrile (B). The autosampler temperature was 4°C, and the injection volume was 2 µL. The Orbitrap Exploris 120 mass spectrometer was operated in information-dependent acquisition mode under the control of Xcalibur software (Thermo) to acquire MS/MS spectra. In this mode, the acquisition software continuously evaluates the full-scan MS spectrum. The electrospray ionization source conditions were set as follows: sheath gas flow rate, 50 Arb; auxiliary gas flow rate, 15 Arb; capillary temperature, 320°C; full MS resolution, 60,000; MS/MS resolution, 15,000; collision energy, stepped normalized collision energy 20/30/40; and spray voltage, 3.8 kV in positive mode and − 3.4 kV in negative mode. Transcriptomic profiling of LR618 Triplicate biological replicates were harvested by rapid centrifugation (8,000 × g, 4°C, 5 min). Cell pellets were immediately flash-frozen in liquid nitrogen and stored at − 80°C until RNA extraction. Total RNA was isolated using the RNeasy Mini Kit (Qiagen, Germany) with on-column DNase I treatment to remove genomic DNA. RNA integrity was verified using an Agilent 2100 Bioanalyzer (RNA Integrity Number, RIN ≥ 9.0), and RNA quantity was measured using a Qubit 4.0 Fluorometer (Thermo Fisher Scientific, USA). Ribosomal RNA was depleted using the MICROBExpress™ Bacterial mRNA Enrichment Kit (Ambion, USA) to enrich mRNA. Strand-specific RNA-seq libraries were prepared using the NEBNext® Ultra™ II Directional RNA Library Prep Kit (NEB, USA) according to the manufacturer’s protocol. Briefly, fragmented RNA (200–300 nt) underwent cDNA synthesis, end repair, adapter ligation, and PCR amplification (12 cycles). Library quality was assessed using an Agilent 2100 Bioanalyzer (average insert size, 370–420 bp) and quantified by qPCR (Kapa Biosystems, USA). Paired-end sequencing (2 × 150 bp) was performed on the Illumina NovaSeq 6000 platform (Novogene, China). Raw reads were preprocessed using Trimmomatic v0.39 to remove adapters and low-quality bases (Phred score < 20). Clean reads were aligned to the L. reuteri DSM 17938 reference genome (NCBI Assembly ASM2698v1) using Bowtie2 v2.4.2 with default parameters. Transcript abundance was quantified using feature counts v2.0.3, and differentially expressed genes (DEGs) were identified using DESeq2 v1.34.0 (|log₂FC| > 2, FDR-adjusted p < 0.05). Functional annotation of DEGs was performed using the KEGG Orthology (KO) and Gene Ontology (GO) databases through clusterProfiler v4.4.4. Validation of glycerol dehydratase overexpression The dhaB operon was amplified from LR618 genomic DNA using primers GDHt -F/R under the following PCR conditions: 98°C for 3 min; 30 cycles of 98°C for 10 s, 55°C for 15 s, and 72°C for 3 min; followed by 72°C for 5 min. The purified amplicon (TransGen Gel Kit) was cloned into pLEM415 by homologous recombination and electroporated into LR618 at 2.0 kV for 5 ms. The transformant (LR620) was selected on erythromycin (10 µg/mL). 3-HPA yields of LR620 and the controls, wild-type LR618 and empty-plasmid LR618/pLEM415, were assayed as described above. For qRT-PCR, RNA from the pre-culture (26 h) and bioconversion (2 h) phases was reverse-transcribed, and dhaB expression was quantified using GDHt -Q-F/R primers with 16S ribosomal RNA (16S-Q-F/R) gene as the reference gene. The qPCR program consisted of 40 cycles at 95°C for 5 s and 60°C for 30 s. Fold changes were calculated using the 2^−ΔΔ Ct method. Exogenous metabolite supplementation The engineered strain LR620 was precultured anaerobically in MRS broth. Single-factor experiments were performed to examine the effects of biotransformation time (0.5–5 h), temperature (23–51°C), inoculum size (10–130 g/L, WCW), pH (4–9), and glycerol concentration (100–800 mM). An orthogonal design was then used to evaluate interactions among these factors. Based on the orthogonal analysis, the most significant factors were further optimized by response surface methodology using a Box-Behnken design. To investigate glycerol dehydratase reactivation, exogenous additives were supplemented into the glycerol solution under the optimized conditions. The individual additives tested were KCl (1–5%), ATP (0.1–0.9%), coenzyme B₁₂ (0.001–0.009%), and MgCl₂ (0.03–0.15%). Based on the optimal individual concentrations, binary, ternary, and quaternary combinations were then tested. All experiments were performed in triplicate. Statistical analyses All data in this study represent the mean (± standard deviation) of three independent experiments. Statistical analyses ( p < 0.05) were performed using SPSS 20.0 to analyze the relationship between metabolomic and transcriptomic data. Origin software was used for graphical analysis. Results and Discussion Carbon source screening identifies galactose as superior for 3-HPA production In the two-step fermentation process using L. reuteri LR618, 3-HPA production varied significantly across carbon sources (Fig. 1 ). Galactose supported the highest titer (8.51 g/L), followed by lactose (7.01 g/L), whereas glucose yielded only 1.67 g/L. Deoxyribose (1.37 g/L) and xylose (1.28 g/L) showed moderate production, whereas maltose, dextrin, soluble starch, and sucrose produced negligible amounts (< 1 g/L). Strikingly, growth curve analysis revealed that glucose supported rapid proliferation, with biomass reaching OD 600 5.1, whereas galactose-cultured cells grew slowly, attaining a maximum OD 600 of only 3.8. This dramatic disparity suggests fundamental differences in how these carbon sources prime cellular metabolism for subsequent glycerol bioconversion. Galactose and lactose, both metabolized through the Leloir pathway [ 17 ], appear to enhance the metabolic capacity for 3-HPA synthesis, whereas glucose and fructose, which preferentially enter glycolysis, do not establish a similarly favorable state. Polysaccharides (starch and dextrin) and disaccharides (sucrose and maltose) likely suffer from inefficient hydrolysis or transport under the experimental conditions, limiting their utility[ 18 ]. Based on these results, galactose and glucose were selected as representative high-performance and baseline carbon sources, respectively, for subsequent multi-omics dissection of the underlying mechanisms. Metabolomic profiling reveals carbon source-dependent metabolic reprogramming To dissect the metabolic basis of the differential 3-HPA production, untargeted metabolomics was performed on samples from four experimental conditions: cells cultured in glucose for 26 h (PLM26), glucose-cultured cells transferred to glycerol for 2 h (PLG2), cells cultured in galactose for 26 h (BAM26), and galactose-cultured cells transferred to glycerol for 2 h (BAG2). This design enabled separate interrogation of the pre-culture phase (carbon source priming) and the bioconversion phase (3-HPA production). Metabolomic profiling revealed 1554 differentially abundant metabolites in the pre-culture phase (Fig. 2 A) and 1650 in the bioconversion phase (Fig. 2 B). Principal component analysis revealed clear separation between the glucose- and galactose-fed groups, indicating substantial metabolic reprogramming induced by carbon source selection. Pathway enrichment analysis identified coenzyme B₁₂ biosynthesis, the Leloir pathway, glycerol metabolism, and redox homeostasis as the key pathways differentiating the two carbon source regimes. The findings indicate that galactose enhances cofactor synthesis and stress resilience in lactic acid bacteria, whereas glucose-driven glycolytic overflow is associated with redox imbalance. In the pre-culture phase, the most significantly enriched pathways were concentrated in amino acid metabolism (alanine, aspartate and glutamate metabolism, beta-alanine metabolism), cofactor and vitamin metabolism (nicotinate and nicotinamide metabolism, pantothenate and CoA biosynthesis), with the above pathways showing high enrichment significance ( P < 0.05) and abundant differential molecules (Fig. 3 A). In galactose-preconditioned cells (BAM26), metabolic priming was characterized by synergistic upregulation of the Leloir pathway and coenzyme B₁₂ biosynthesis. Elevated levels of UDP-galactose, a key Leloir intermediate, were observed (2.3-fold vs. PLM26, p < 0.01), accompanied by accumulation of 5′-deoxyadenosylcobalamin, a coenzyme B₁₂ precursor. Concurrent enrichment of the methionine salvage and selenocompound metabolism pathways supplied ATP and selenocysteine, both essential for coenzyme B₁₂ synthesis. This coordinated metabolic configuration ensured that, upon transfer to glycerol, both GDHt and its essential cofactor were readily available [ 19 ]. In contrast, glucose-preconditioned cells (PLM26) exhibited metabolic suppression characterized by accumulation of acidic byproducts (acetate and lactate) and depletion of nucleotide sugars. This acidification may trigger stress responses that downregulate nonessential pathways, whereas CCR mediated by the Crc system actively silences genes involved in alternative carbon utilization [ 20 ]. The absence of UDP-galactose and coenzyme B₁₂ precursors in PLM26 indicates that glucose metabolism fails to establish the metabolic infrastructure required for efficient 3-HPA synthesis. In sharp contrast, the strain exhibited profound metabolic network remodeling during glycerol bioconversion for 3-HPA production, with core enriched pathways shifting to those directly associated with 3-HPA biosynthesis—alanine, aspartate and glutamate metabolism remained highly significant, alongside robust enrichment of glycerol catabolism, coenzyme regeneration and energy supply pathways, and sustained significant enrichment of stress resistance-related pathways (Fig. 3 B). Upon transfer to glycerol, galactose-fed cells (BAG2) achieved 5.1-fold higher 3-HPA titers than glucose-fed cells (PLG2), driven by different metabolic configurations. In BAG2, glycerol was preferentially channeled through the reductive pathway, as indicated by elevated GDHt activity markers and reduced glycerol-3-phosphate accumulation (1.2-fold downregulation of glycerol kinase flux). Critically, redox analysis revealed a more oxidative NAD⁺/NADH ratio in BAG2 (2.3) than in PLG2 (1.6, p < 0.05), which suppressed PDOR-mediated diversion of 3-HPA to 1,3-propanediol. This favorable redox state originated from Leloir pathway activity during pre-culture, which generates NADH in a controlled manner without the glycolytic overflow characteristic of glucose metabolism. Glucose-fed cells (PLG2) displayed metabolic dysregulation characterized by ATP depletion and NADH overload. Excess NADH activated PDOR, redirecting 3-HPA toward 1,3-propanediol as a redox sink. At the same time, acid stress caused by organic acids produced during pre-culture persisted and further compromised metabolic efficiency [ 21 ]. UDP-galactose, a key intermediate of the Leloir pathway, accumulated to 2.3-fold higher levels in galactose-fed cells than in glucose-fed controls ( p < 0.01). Similarly, S-adenosylmethionine (SAM) reserves were 2.1-fold higher in galactose-preconditioned cells ( p < 0.05) and persisted during bioconversion. The NAD⁺/NADH ratio was also significantly elevated in galactose-fed bioconversion cultures (2.3) compared with glucose-fed controls (1.6, p < 0.05). These metabolite signatures—UDP-galactose accumulation, SAM enrichment, and a more oxidized redox state—collectively distinguished the metabolic configuration established by galactose from that of glucose. The mechanistic implications of these metabolic differences are further explored through transcriptomic analysis in the following section. Transcriptomic reprogramming of glycerol metabolism by galactose To complement the metabolomic findings and establish the transcriptional basis of the metabolic advantages conferred by galactose, comparative transcriptomics was performed on the same four experimental conditions. RNA-seq analysis identified 346 differentially expressed genes between the galactose- and glucose-fed groups (|log₂FC| > 1, FDR-adjusted p < 0.05), Hierarchical clustering showed clear transcriptional specialization under galactose conditions, with BAM26 and BAG2 forming a cohesive cluster. Transcriptomic profiling revealed 374 differentially expressed metabolism-associated transcripts across the experimental conditions in the pre-culture phase and 331 in the bioconversion phase (Fig. 4 A, B). During the bioconversion phase, galactose feeding resulted in 3.1-fold upregulation of GDHt (EC 4.2.1.30) gene in BAG2 relative to PLG2 ( p < 0.05) (Fig. 5 ). This transcriptional elevation directly correlated with the enhanced 3-HPA synthesis capacity observed in the metabolomic analysis. Importantly, this upregulation was not limited to GDHt alone. Genes encoding enzymes required for coenzyme B₁₂ biosynthesis were also co-induced during the pre-culture phase, with cobA (C6H63_RS00365) and cobQ (C6H63_RS00370) upregulated 2.6- and 3.3-fold, respectively, in BAM26 relative to PLM26 ( p < 0.05). This coordinated transcriptional program indicates that glycerol exposure significantly enhances the availability of both the GDHt and its essential cofactor coenzyme B₁₂, a key regulatory axis driving glycerol-dependent 3-HPA biosynthesis in Limosilactobacillus reuteri that was comprehensively characterized in the previous work [ 22 ]. Galactose metabolism was driven by 4.2-fold induction of Leloir pathway genes ( galK , C6H63_RS00285; galT , C6H63_RS00290), enabling efficient galactose catabolism to generate ATP and NADH without the glycolytic overflow characteristic of glucose metabolism. At the same time, genes encoding the oxidative glycerol pathway ( glpK , C6H63_RS00450; glpD , C6H63_RS00455) were downregulated 2.5-fold in galactose-fed cells, thereby minimizing carbon diversion toward glycolysis and ensuring that glycerol entering the cell was preferentially directed toward 3-HPA synthesis. This transcriptional configuration, activation of the desired pathway coupled with suppression of competing routes, represents a more favorable metabolic state that glucose metabolism fails to establish. The co-upregulation of dhaB and cob genes suggests a transcriptionally coupled mechanism that optimizes GDHt function. This dual regulation differs from previous L. reuteri studies in which coenzyme B₁₂ limitation constrained GDHt activity despite adequate enzyme expression [ 23 ], highlighting the unique ability of galactose to bypass this bottleneck. The transcriptional coupling likely arises from shared regulatory elements responsive to UDP-galactose or related metabolites, thereby linking carbon source availability to both pathway enzyme production and cofactor synthesis. CCR in L. reuteri is mediated by the Crc system, which actively represses genes related to alternative carbon utilization in glucose-grown cells. In galactose-fed cells, crc transcript levels were reduced 1.5-fold ( p < 0.05), consistent with alleviation of CCR. The accumulation of UDP-galactose, which does not occur during glucose metabolism, correlated with this derepression, suggesting that this metabolite or one of its derivatives directly or indirectly modulates Crc activity (Fig. 6 ). This UDP-galactose-Crc regulatory axis represents a previously unreported mechanism linking carbon source identity to pathway activation in lactic acid bacteria. The transcriptomic data provide a mechanistic explanation for the metabolomic observations. The elevated GDHt activity inferred from metabolite profiles in BAG2 directly reflects transcriptional upregulation of dhaB . The favorable NAD⁺/NADH ratio in galactose-fed cells arises from induction of the Leloir pathway, which generates NADH without the excessive glycolytic flux characteristic of glucose metabolism. Sustained coenzyme B₁₂ availability during bioconversion results from induction of cob genes during pre-culture, combined with SAM-mediated stabilization. Together, these data indicate that galactose orchestrates a multilayered metabolic program, transcriptional, enzymatic, and metabolic, that collectively optimizes 3-HPA synthesis. Hierarchical metabolic constraints govern 3-HPA biosynthetic efficiency The multi-omics framework described above predicted that efficient 3-HPA production requires coordinated resolution of all three components of the metabolic trilemma: coenzyme B₁₂ availability, redox balance, and transcriptional derepression. To test this prediction and translate mechanistic insights into engineering strategies, systematic phenotypic analyses were performed across engineered and supplemented L. reuteri strains. To validate the rate-limiting role of GDHt predicted by transcriptomics, strain LR620 was constructed by overexpressing the dhaB operon in LR618 using plasmid pLEM415. qRT-PCR confirmed 4.5-fold upregulation of dhaB in LR620 relative to the wild type ( p < 0.05) (Fig. 7 A). In the glucose-based two-step fermentation system, LR620 produced 5.77 g/L 3-HPA, a 3.45-fold increase over wild-type LR618 (1.67 g/L, p < 0.05), confirming GDHt as a key bottleneck (Fig. 7 B). However, two observations indicated that GDHt overexpression alone was insufficient to achieve galactose-level performance. First, the empty-plasmid control (LR618/pLEM415) yielded only 1.42 g/L, which was 15% lower than the wild type ( p < 0.05), directly demonstrating a measurable ATP burden caused by plasmid maintenance. This 15% deficit is consistent with the metabolomic observation of energy depletion in glucose-fed systems, in which ATP scarcity constrains multiple metabolic processes. Second, despite elevated GDHt expression, LR620 showed no increase in endogenous coenzyme B₁₂ synthesis, with qRT-PCR showing unchanged cobA expression relative to the wild type (1.02-fold, p < 0.05). This uncoupling of enzyme overexpression from cofactor biosynthesis, a feature inherent to galactose metabolism but absent in glucose-based engineering, explains why the LR620 yield (5.77 g/L) remained below that achieved with galactose (8.51 g/L) despite higher GDHt transcript levels [ 24 ]. After establishing that enzyme availability is necessary but insufficient, systematic optimization of biotransformation parameters was carried out using LR620. Initial single-factor experiments established the baseline ranges of biotransformation time (0.5–5 h), temperature (23–51°C), inoculum size (10–130 g/L, WCW), pH (4–9), and glycerol concentration (100–800 mM). To capture interactions among factors, an L₁₆(3⁵) orthogonal design was implemented. ANOVA revealed that inoculum size and glycerol concentration exerted the strongest effects on 3-HPA production, followed by temperature and pH, whereas biotransformation time within the 1.5–3.0 h range was not significant. The optimal combination identified by range analysis, 110 g/L inoculum size, 700 mM glycerol, 30°C, pH 6.0, and 2.0 h, increased the titer to 18.54 g/L. Response surface methodology using a Box-Behnken design was then applied to refine the three most significant factors: inoculum size, glycerol concentration, and temperature. The quadratic model showed an excellent fit (R² = 0.9957, adjusted R² = 0.9901) and a non-significant lack of fit ( p < 0.05), validating its predictive capacity. Model coefficients revealed significant positive linear effects of inoculum size ( p < 0.05) and glycerol concentration ( p < 0.05), as well as significant negative quadratic effects for all three factors ( p < 0.05), indicating the existence of optimal intermediate values. The interaction term inoculum size with temperature was significant ( p < 0.05), demonstrating that the effect of inoculum size depends on temperature, likely reflecting a trade-off between enzyme availability at high cell density and temperature-sensitive mass transfer efficiency. The model predicted a maximum 3-HPA production of 23.5 g/L at 102 g/L inoculum size (WCW), 724 mM glycerol, and 28.9°C. Experimental validation under these conditions yielded 22.79 g/L, closely matching the prediction and representing a 3.95-fold improvement over the initial unoptimized condition (5.77 g/L for LR620 without optimization). This progression, from 5.77 to 18.54 to 22.79 g/L, demonstrates the value of hierarchical optimization that progressively incorporates additional layers of system complexity. Despite achieving 22.79 g/L through parameter optimization, 3-HPA accumulation remained transient, peaking at 2.0 h and declining thereafter. This pattern reflects the well-characterized suicide inactivation of GDHt. Upon binding glycerol, the enzyme induces a conformational change in coenzyme B₁₂, leading to irreversible cofactor-enzyme complexation and loss of catalytic activity [ 25 ]. Reactivation requires removal of the damaged cofactor and replacement with fresh coenzyme B₁₂, an ATP-dependent process facilitated by Mg²⁺ ions, whereas K⁺ is essential for maintaining active-site conformation and substrate positioning [ 26 ]. To overcome this limitation, exogenous additives were tested individually under the optimized biotransformation conditions. KCl supplementation, as a K⁺ source, enhanced 3-HPA production in a dose-dependent manner, with an optimum at 3% (w/v), yielding 24.33 g/L, a 6.76% increase over the response surface methodology-optimized baseline (Fig. 8 A). Exogenous ATP supplementation was investigated to directly address the energy demand associated with GDHt reactivation. ATP showed a clear parabolic effect (Fig. 8 B), with 3-HPA production peaking at 0.3% ATP (25.11 g/L, 10% above baseline) and declining at higher concentrations. Mechanistically, low ATP concentrations may promote GDHt reactivation by driving ejection of damaged coenzyme B₁₂ [ 27 ], whereas excess ATP may inhibit GAPDH and disrupt energy homeostasis [ 28 ], thereby redirecting carbon flux away from glycerol assimilation. These findings confirm the energy limitation inferred from the 15% plasmid burden effect and indicate that ATP improves production only within a narrow optimal range. Coenzyme B₁₂ supplementation was more effective. Titers increased progressively to 25.56 g/L at 0.005% (w/v), representing a 12% improvement (Fig. 8 C). Notably, the response was also parabolic, because concentrations above 0.005% (0.007–0.009%) led to progressively lower titers, suggesting that supra-optimal B₁₂ may compete with native cofactor for binding without contributing to productive catalysis. MgCl₂ supplementation showed a narrower optimum, with peak production (23.21 g/L) at 0.06% (w/v) and sharp declines at higher concentrations, likely because of osmotic stress (Fig. 8 D). Based on the optimal individual concentrations of each component, various combinatorial supplementations were then investigated to identify potential synergistic effects. The quaternary combination (ATP + KCl + B₁₂ + MgCl₂) produced the highest titer, 28.96 g/L, corresponding to a 27% improvement over the response surface methodology-optimized baseline (22.79 g/L) and a 17.34-fold increase over the initial unoptimized condition (1.67 g/L for wild-type LR618) (Table 1 ). Table 1 3-HPA Production of Limosilactobacillus reuteri under Different Treatments. Region Treatments 3-HPA LR618 glucose as the carbon source 1.67 g/L LR618 galactose as the carbon source 8.51 g/L LR618/pLEM415 with the pLEM415 1.42 g/L LR620 overexpression of GDHt 5.77 g/L LR620 optimization of fermentation engineering 22.79 g/L LR620 with 0.3% ATP 25.11 g/L LR620 with 3% KCl 24.33 g/L LR620 with 0.005% coenzyme B₁₂ 25.56 g/L LR620 with 0.06% MgCl₂ 23.21 g/L LR620 with ATP, KCl, coenzyme B₁₂, MgCl₂ 28.96 g/L Mechanistically, these results establish a hierarchical framework for GDHt function. The reactivation process itself, removal of damaged B₁₂ and replacement with fresh cofactor, requires ATP and Mg²⁺ and is enabled by exogenous B₁₂ supplementation. However, once reactivated, the enzyme must rapidly bind glycerol and convert it to 3-HPA, a catalytic step critically dependent on K⁺ for active-site integrity. Thus, K⁺ acts as a gatekeeper that translates reactivation events into productive catalysis [ 29 ]. In its absence, even successfully reactivated enzyme molecules show suboptimal activity, thereby limiting total 3-HPA production. The additive concentrations (3% KCl, 0.3% ATP, 0.005% B₁₂, 0.06% MgCl₂) represent economically viable levels compatible with downstream processing. The systematic phenotypic analyses provide quantitative validation of the multi-omics framework and explain why single-gene engineering alone cannot achieve galactose-level performance. The performance gap between LR620 (22.79 g/L after process optimization) and the fully supplemented system (28.96 g/L) reflects three fundamental limitations. First, cellular energy status imposes a measurable constraint. The empty-plasmid control produced 15% less 3-HPA than wild-type LR618 (1.42 vs. 1.67 g/L), directly demonstrating that ATP diversion to plasmid replication and selection marker expression restricts energy-intensive processes. The parabolic response to exogenous ATP supplementation, peak at 0.3% (25.11 g/L) and decline at ≥ 0.5%, further shows that energy availability must be carefully balanced. Insufficient ATP limits GDHt reactivation, whereas excess ATP may inhibit GAPDH and disrupt central metabolism [ 28 ]. This narrow optimal range explains why ATP supplementation alone, without coordinated optimization of cofactors and ions, cannot achieve maximal yields. Second, enzyme overexpression without cofactor enhancement creates an unbalanced metabolic state. Despite 4.5-fold higher dhaB expression, LR620 showed no increase in endogenous coenzyme B₁₂ synthesis, as cobA expression remained unchanged. This uncoupling means that, although more GDHt apoenzyme is available, the supply of its essential cofactor remains limiting. This is a classic example of hierarchical metabolic constraint in which downstream capacity cannot compensate for an upstream limitation [ 30 ]. The 3.45-fold improvement from GDHt overexpression (5.77 vs. 1.67 g/L) was therefore substantially lower than the 5.1-fold advantage achieved through galactose’s coordinated program (8.51 vs. 1.67 g/L). Third, the ionic and cofactor environment imposes its own constraints. The parabolic responses to exogenous coenzyme B₁₂ and ATP, the narrow optimal range for Mg²⁺, and the indispensable role of K⁺ in translating reactivation into catalysis (detailed in Section 3.4.3) collectively reveal that each component of the GDHt functional cycle must be precisely balanced. Together with the ATP burden from plasmid maintenance and the uncoupling of enzyme overexpression from cofactor synthesis, these observations establish that hierarchical metabolic constraints—spanning energy supply, cofactor availability, and ionic environment—cannot be overcome by single-gene engineering alone [ 30 ]. Finally, The multi-omics analyses and phenotypic validations presented above collectively demonstrate that galactose’s native superiority arises from an integrated regulatory program that simultaneously addresses coenzyme B₁₂ availability, redox balance, and carbon catabolite repression. This coordinated tripartite mechanism achieves what reductionist engineering strategies cannot: a self-reinforcing metabolic state in which enzyme supply, cofactor availability, and ionic environment are optimized concurrently. These findings shift the paradigm of microbial aldehyde production away from an “enzyme-centric” approach—focused on overexpressing a single rate-limiting step—toward a “systems-centric” framework in which cofactor dynamics, energy management, and ionic homeostasis dominate yield optimization. Future efforts should prioritize chromosomal integration of GDHt- cobA operons coupled with redox-balancing modules, such as NADH oxidase expression or Leloir pathway reconstruction in glucose-grown cells, to eliminate plasmid burden and recapitulate the integrated advantages of galactose in cost-effective glucose-based processes. Conclusion This study elucidates a previously unreported cross-phase regulatory axis in L. reuteri , in which galactose optimizes 3-HPA biosynthesis through an integrated tripartite mechanism that alleviates CCR, balances ATP/NADH pools, and coordinates holoenzyme assembly—together achieving a 5.1-fold increase over glucose-grown cultures. Translating these mechanistic insights into engineering strategies demonstrated that single-gene overexpression alone cannot overcome hierarchical metabolic constraints. Instead, synergistic supplementation with ATP, K⁺, coenzyme B₁₂, and Mg²⁺, which addresses enzyme reactivation, cofactor availability, and catalytic efficiency, enabled a 17.34-fold improvement, reaching a final titer of 28.96 g/L. To our knowledge, this is the highest 3-HPA titer reported in Limosilactobacillus reuteri in shake-flask fermentation to date. The final optimized process consisted of cell harvest at 24 h, biotransformation at 28.9°C for 2.0 h with 102 g/L inoculum size (WCW) and 724 mM glycerol (pH 6.0), and supplementation with 3% KCl, 0.3% ATP, 0.005% coenzyme B₁₂, and 0.06% MgCl₂. While achieved at shake-flask scale, this titer provides a strong foundation for bioreactor scale-up studies. This work establishes a systems-level blueprint for reprogramming probiotic chassis and demonstrates that resolving the metabolic trilemma of cofactor dependence, redox imbalance, and CCR is essential for next-generation sustainable antimicrobial production. Abbreviations 3-HPA 3-hydroxypropionaldehyde GDHt glycerol dehydratase CCR carbon catabolite repression qRT‒PCR quantitative real‑time PCR WCW wet cell weight MRS Man-Rogosa-Sharpe PDOR propanediol oxidoreductase SAM S-adenosylmethionine Declarations Ethics approval and consent to participate Not applicable. Consent for publication Not applicable. Availability of data and materials All data generated or analyzed during this study are included in this published article. Competing interests The authors declare that they have no competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Funding This publication was supported by the Project of Science and Technology Innovation Team in Anhui Academy of Agricultural Sciences (No. 2025YL079). Authors’ contributions Qiang Yin designed the project, carried out experiments, and drafted the manuscript. Liang Li, Yong Fang and FuQiang Chengcarried out experiments. Hang Wu revised the manuscript. Zuojun Liu and Ting Guo supervised the project and revised the manuscript. All authors read and approved the final manuscript. Declaration of competing interest The authors declare no conflict of interest. Acknowledgments Not applicable. References Stevens MJ, Vollenweider S, Meile L, Lacroix C: 1,3-Propanediol dehydrogenases in Lactobacillus reuteri : impact on central metabolism and 3-hydroxypropionaldehyde production. Microb Cell Fact. 2011;10:61. doi: 10.1186/1475-2859-10-61. Liang N, Neužil-Bunešová V, Tejnecký V, Gänzle M, Schwab C: 3-Hydroxypropionic acid contributes to the antibacterial activity of glycerol metabolism by the food microbe Limosilactobacillus reuteri . Food Microbiol. 2021;98:103720. doi: 10.1016/j.fm.2020.103720. Kumar V, Ashok S, Park S: Recent advances in biological production of 3-hydroxypropionic acid. Biotechnol Adv 2013; 31:945–961. doi:10.1016/j.biotechadv.2013.02.008. 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Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 08 May, 2026 Reviews received at journal 17 Apr, 2026 Reviews received at journal 10 Apr, 2026 Reviewers agreed at journal 03 Apr, 2026 Reviewers agreed at journal 01 Apr, 2026 Reviewers invited by journal 01 Apr, 2026 Editor assigned by journal 31 Mar, 2026 Submission checks completed at journal 31 Mar, 2026 First submitted to journal 30 Mar, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {\"props\":{\"pageProps\":{\"initialData\":{\"identity\":\"rs-9269523\",\"acceptedTermsAndConditions\":true,\"allowDirectSubmit\":false,\"archivedVersions\":[],\"articleType\":\"Research Article\",\"associatedPublications\":[],\"authors\":[{\"id\":617009380,\"identity\":\"f6b90cf5-480a-483a-9230-5ca5a76786ac\",\"order_by\":0,\"name\":\"Zuojun Liu\",\"email\":\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAyklEQVRIiWNgGAWjYBACPmYgIWEgIcfP3tj48AMxWtjAWgpsjCV7DjcbSxClBUx+SEvccCO9TYCHKC3sPMYfLAwOM264+bCNQYLBTk63gaDDeAwMJAwOM0veTmx7UMCQbGx2gAgtCUAtbHy3E9sNJBgOJG4jRssBoBYehpsH2yR4iNRi2CBhkCYhcIORaC1sxcB4sTGQ7EkEBrIBEX7h5z+8+bPEH4n6fvbjDx9+qLCTI6gFBJgRMWhAhHIQYCQqnYyCUTAKRsHIBQCK8zrQnVC8JwAAAABJRU5ErkJggg==\",\"orcid\":\"\",\"institution\":\"Anhui Academy of Agricultural Sciences\",\"correspondingAuthor\":true,\"prefix\":\"\",\"firstName\":\"Zuojun\",\"middleName\":\"\",\"lastName\":\"Liu\",\"suffix\":\"\"},{\"id\":617009381,\"identity\":\"6271a29d-4734-4ba5-945b-0647a5a83fe0\",\"order_by\":1,\"name\":\"Qiang Yin\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Anhui Academy of Agricultural Sciences\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Qiang\",\"middleName\":\"\",\"lastName\":\"Yin\",\"suffix\":\"\"},{\"id\":617009382,\"identity\":\"b0164d2b-d105-4c5a-86e3-332ccfb3da28\",\"order_by\":2,\"name\":\"Liang Li\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Anhui Academy of Agricultural Sciences\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Liang\",\"middleName\":\"\",\"lastName\":\"Li\",\"suffix\":\"\"},{\"id\":617009383,\"identity\":\"af6bdc60-7eee-4377-b932-ccb04b773c1b\",\"order_by\":3,\"name\":\"Hang Wu\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Anhui University\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Hang\",\"middleName\":\"\",\"lastName\":\"Wu\",\"suffix\":\"\"},{\"id\":617009384,\"identity\":\"12a45580-b89d-465b-b6d4-c1309016fb4a\",\"order_by\":4,\"name\":\"Yong Fang\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Anhui Academy of Agricultural Sciences\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Yong\",\"middleName\":\"\",\"lastName\":\"Fang\",\"suffix\":\"\"},{\"id\":617009385,\"identity\":\"8b92b12d-d9af-4300-a2f8-3353e4ccfe07\",\"order_by\":5,\"name\":\"Fuqiang Cheng\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Anhui Academy of Agricultural Sciences\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Fuqiang\",\"middleName\":\"\",\"lastName\":\"Cheng\",\"suffix\":\"\"},{\"id\":617009386,\"identity\":\"104a42a4-fbf4-4307-a4d7-7e81a51e1759\",\"order_by\":6,\"name\":\"Ting Guo\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Jiangsu Academy of Agricultural Sciences\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Ting\",\"middleName\":\"\",\"lastName\":\"Guo\",\"suffix\":\"\"}],\"badges\":[],\"createdAt\":\"2026-03-30 16:11:24\",\"currentVersionCode\":1,\"declarations\":\"\",\"doi\":\"10.21203/rs.3.rs-9269523/v1\",\"doiUrl\":\"https://doi.org/10.21203/rs.3.rs-9269523/v1\",\"draftVersion\":[],\"editorialEvents\":[],\"editorialNote\":\"\",\"failedWorkflow\":false,\"files\":[{\"id\":106403005,\"identity\":\"067846fd-2f33-4b0e-abb2-12a6d9fe298c\",\"added_by\":\"auto\",\"created_at\":\"2026-04-08 09:13:21\",\"extension\":\"jpeg\",\"order_by\":1,\"title\":\"Figure 1\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":95687,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e3-HPA production from different carbon sources in the two-step fermentation system using LR618. The error bars represent the standard deviation.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"image1.jpeg\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-9269523/v1/8b89a52042c64a77cab100ca.jpeg\"},{\"id\":106215758,\"identity\":\"21c85190-9b0a-45a9-b3ea-c4db7d6c0c3d\",\"added_by\":\"auto\",\"created_at\":\"2026-04-06 08:27:39\",\"extension\":\"jpeg\",\"order_by\":2,\"title\":\"Figure 2\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":283566,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eVolcano plots showing differential metabolites in\\u003cem\\u003e \\u003c/em\\u003eLR618 fermentation between glucose and galactose conditions.\\u003c/p\\u003e\\n\\u003cp\\u003e(A) Pre-culture phase; (B) Bioconversion phase. Red: significantly upregulated; blue: significantly downregulated (|log₂FC| \\u0026gt; 1, \\u003cem\\u003ep\\u003c/em\\u003e \\u0026lt; 0.05); gray: not significant. Dashed lines indicate significance thresholds.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"image2.jpeg\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-9269523/v1/62ea608bb0a887af0ca217df.jpeg\"},{\"id\":106215759,\"identity\":\"316b77a4-7954-47c0-9a5f-e96ee6c012b5\",\"added_by\":\"auto\",\"created_at\":\"2026-04-06 08:27:39\",\"extension\":\"jpeg\",\"order_by\":3,\"title\":\"Figure 3\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":325180,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eKEGG pathway enrichment treemaps of differentially expressed metabolites in LR618 fermentation between glucose and galactose conditions.\\u003c/p\\u003e\\n\\u003cp\\u003e(A) Pre-culture phase; (B) Bioconversion phase. The area of each rectangle represents the number of differential genes/metabolites enriched in the corresponding pathway, and the color intensity indicates the enrichment significance (–ln\\u003cem\\u003eP\\u003c/em\\u003e-value), with darker colors reflecting higher significance.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"image3.jpeg\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-9269523/v1/aedd1bd0d9268f5398ef7e65.jpeg\"},{\"id\":106403240,\"identity\":\"0b82fffa-bd6f-4848-ae64-dbb9f9623390\",\"added_by\":\"auto\",\"created_at\":\"2026-04-08 09:13:57\",\"extension\":\"jpeg\",\"order_by\":4,\"title\":\"Figure 4\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":255615,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eTranscriptomic volcano plots of transcriptomic changes in LR618 fermentation between glucose and galactose conditions.\\u003c/p\\u003e\\n\\u003cp\\u003e(A) Pre-culture phase; (B) Bioconversion phase. The x-axis shows log₂(fold change) in gene expression, and the y-axis shows -log₁₀(adjusted P-value, padj). Red: up-regulated; blue: down-regulated (|log₂FC| \\u0026gt; 1, \\u003cem\\u003ep\\u003c/em\\u003e \\u0026lt; 0.05); gray: not significant. Selected differentially expressed genes are annotated.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"image4.jpeg\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-9269523/v1/1e0c2b3ebff7f77cb194bffe.jpeg\"},{\"id\":106215760,\"identity\":\"7d555ada-bfa4-456d-954c-bb8a1e64cf1d\",\"added_by\":\"auto\",\"created_at\":\"2026-04-06 08:27:39\",\"extension\":\"png\",\"order_by\":5,\"title\":\"Figure 5\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":306276,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eKEGG glycerolipid metabolism pathway map of LR618, showing transcriptomic changes in the glycerol-to-3-HPA biosynthetic pathway.Colored boxes indicate differentially expressed genes between galactose and glucose carbon source cultures during 3-HPA fermentation, with green for upregulation and red for downregulation in galactose-fed groups. EC numbers are labeled in boxes, and arrows represent metabolic reactions.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"image5.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-9269523/v1/7db95ad008c2d8e9f146234d.png\"},{\"id\":106215763,\"identity\":\"f6184f56-aa4b-4d5d-8741-2e3d156a3002\",\"added_by\":\"auto\",\"created_at\":\"2026-04-06 08:27:39\",\"extension\":\"png\",\"order_by\":6,\"title\":\"Figure 6\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":109431,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eKEGG galactose metabolism pathway map of LR618. Colored boxes show differentially expressed genes/metabolites between galactose and glucose carbon source cultures, with green for upregulation and red for downregulation in galactose-fed groups. Numbers indicate enzyme commission (EC) numbers, and arrows represent metabolic reactions.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"image6.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-9269523/v1/82a359468b4e66d85866e0eb.png\"},{\"id\":106215764,\"identity\":\"ccaffa8e-6c3b-486e-a828-90fed63129a8\",\"added_by\":\"auto\",\"created_at\":\"2026-04-06 08:27:39\",\"extension\":\"jpeg\",\"order_by\":7,\"title\":\"Figure 7\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":61775,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eTranscriptional levels of key genes and 3-HPA production in \\u003cem\\u003eLactobacillus reuteri\\u003c/em\\u003e strains.\\u003c/p\\u003e\\n\\u003cp\\u003e(A) Relative transcription levels of \\u003cem\\u003edhaB\\u003c/em\\u003e and \\u003cem\\u003ecobA\\u003c/em\\u003ein LR620 and LR618. (B) 3-HPA production titers of LR618, LR620, and LR618/pLEM415. The errorbars represent the standard deviation.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"image7.jpeg\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-9269523/v1/e9384333e133fb612dbbc3c3.jpeg\"},{\"id\":106215765,\"identity\":\"bd540374-4b0c-466f-9488-a63bf662628c\",\"added_by\":\"auto\",\"created_at\":\"2026-04-06 08:27:39\",\"extension\":\"jpeg\",\"order_by\":8,\"title\":\"Figure 8\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":209236,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eEffect of single-factor supplementation on 3-HPA production by LR620 under optimized biotransformation conditions.\\u003c/p\\u003e\\n\\u003cp\\u003e(A) KCl, (B) ATP, (C) coenzyme B₁₂, (D) MgCl₂. The error bars represent the standard deviation.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"image8.jpeg\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-9269523/v1/3a7486502b2aa0b19d7c092f.jpeg\"},{\"id\":106405677,\"identity\":\"a638319f-4b76-418d-bca7-bb46a1577d89\",\"added_by\":\"auto\",\"created_at\":\"2026-04-08 09:28:07\",\"extension\":\"pdf\",\"order_by\":0,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"manuscript-pdf\",\"size\":2576626,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"manuscript.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-9269523/v1/70e99fe6-80c8-43e1-8729-65f7a005db8b.pdf\"}],\"financialInterests\":\"No competing interests reported.\",\"formattedTitle\":\"Multi-omics-guided metabolic engineering of Limosilactobacillus reuteri for high-level 3-hydroxypropionaldehyde production from glucose\",\"fulltext\":[{\"header\":\"Background\",\"content\":\"\\u003cp\\u003eAs antimicrobial resistance increases, interest in sustainable alternatives to conventional antibiotics has intensified, and probiotic-derived antimicrobials have gained attention within \\u0026ldquo;One Health\\u0026rdquo; strategies. \\u003cem\\u003eLimosilactobacillus reuteri\\u003c/em\\u003e, a commensal bacterium widely distributed in the gastrointestinal tracts of vertebrates, produces reuterin, a broad-spectrum antimicrobial agent comprising hydrated, non-hydrated, and dimeric forms of 3-hydroxypropionaldehyde (3-HPA). This compound exhibits activity against both Gram-positive and Gram-negative bacteria, fungi, and protozoa, providing \\u003cem\\u003eL. reuteri\\u003c/em\\u003e with a competitive advantage in its ecological niches [\\u003cspan citationid=\\\"CR1\\\" class=\\\"CitationRef\\\"\\u003e1\\u003c/span\\u003e]. Beyond its antimicrobial properties, 3-HPA also holds considerable promise as a biochemical precursor for the synthesis of valuable compounds such as acrylic acid and functional polymers, highlighting its industrial potential [\\u003cspan citationid=\\\"CR2\\\" class=\\\"CitationRef\\\"\\u003e2\\u003c/span\\u003e].\\u003c/p\\u003e \\u003cp\\u003eDespite this considerable translational potential, scaling 3-HPA biosynthesis in \\u003cem\\u003eL. reuteri\\u003c/em\\u003e remains difficult because of fundamental metabolic constraints that limit industrial viability. In this organism, 3-HPA production is intrinsically linked to glycerol metabolism, a pathway constrained by several interconnected limitations [\\u003cspan additionalcitationids=\\\"CR4\\\" citationid=\\\"CR3\\\" class=\\\"CitationRef\\\"\\u003e3\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR6\\\" class=\\\"CitationRef\\\"\\u003e5\\u003c/span\\u003e]. First, the process is strictly dependent on coenzyme B₁₂ because the catalytic activity of glycerol dehydratase (GDHt), the key enzyme in the pathway, requires this cofactor for optimal function [\\u003cspan citationid=\\\"CR7\\\" class=\\\"CitationRef\\\"\\u003e6\\u003c/span\\u003e]. However, \\u003cem\\u003eL. reuteri\\u003c/em\\u003e possesses only limited endogenous capacity for B₁₂ synthesis, resulting in suboptimal enzyme activation [\\u003cspan citationid=\\\"CR8\\\" class=\\\"CitationRef\\\"\\u003e7\\u003c/span\\u003e]. Second, pronounced redox imbalance arises because NADH accumulates during glycerol metabolism, which promotes the diversion of 3-HPA toward 1,3-propanediol via NADH-dependent propanediol oxidoreductase (PDOR), thereby substantially reducing 3-HPA yield [\\u003cspan citationid=\\\"CR9\\\" class=\\\"CitationRef\\\"\\u003e8\\u003c/span\\u003e]. Third, carbon catabolite repression (CCR) mediated by global regulators such as Crc suppresses glycerol metabolic genes in the presence of preferred carbon sources such as glucose, drastically limiting carbon flux into the 3-HPA biosynthetic pathway [\\u003cspan citationid=\\\"CR10\\\" class=\\\"CitationRef\\\"\\u003e9\\u003c/span\\u003e]. These three limitations, coenzyme B₁₂ scarcity, redox stress, and transcriptional repression, collectively form a tightly interconnected \\u0026ldquo;metabolic trilemma\\u0026rdquo; that complicates engineering strategies for enhanced 3-HPA production.\\u003c/p\\u003e \\u003cp\\u003eCurrent research specifically targeting 3-HPA enhancement in \\u003cem\\u003eL. reuteri\\u003c/em\\u003e remains limited. Although one study used carboxyl-functionalized superparamagnetic nanoparticles attached to the bacterial surface to improve conversion efficiency, this strategy increases operational complexity and cost [\\u003cspan citationid=\\\"CR5\\\" class=\\\"CitationRef\\\"\\u003e10\\u003c/span\\u003e]. Carbon source selection represents an underexplored yet potentially transformative lever for metabolic optimization. Carbon sources are pivotal determinants of microbial metabolic profiles because they dictate the distribution of carbon flux across pathways [\\u003cspan citationid=\\\"CR12\\\" class=\\\"CitationRef\\\"\\u003e11\\u003c/span\\u003e]. Glucose, a preferred carbon source for most microorganisms, rapidly activates glycolysis but often triggers CCR, thereby suppressing genes involved in alternative metabolic routes [\\u003cspan citationid=\\\"CR13\\\" class=\\\"CitationRef\\\"\\u003e12\\u003c/span\\u003e]. Heterologous expression of 3-HPA biosynthesis pathways in \\u003cem\\u003eEscherichia coli\\u003c/em\\u003e has also been attempted to circumvent native regulation in \\u003cem\\u003eL. reuteri\\u003c/em\\u003e, but these approaches sacrifice the probiotic compatibility required for food and pharmaceutical applications [\\u003cspan citationid=\\\"CR14\\\" class=\\\"CitationRef\\\"\\u003e13\\u003c/span\\u003e].\\u003c/p\\u003e \\u003cp\\u003eTranslating 3-HPA production to an industrially viable scale requires improvements in both titer and cost. In other microorganisms such as \\u003cem\\u003eKlebsiella pneumoniae\\u003c/em\\u003e, metabolic engineering for 1,3-propanediol production has achieved notable success, validating key strategies including cofactor rebalancing and endogenous cofactor synthesis [\\u003cspan citationid=\\\"CR15\\\" class=\\\"CitationRef\\\"\\u003e14\\u003c/span\\u003e]. Zhang et al. engineered \\u003cem\\u003eK. pneumoniae\\u003c/em\\u003e to efficiently convert glycerol to 1,3-propanediol by reprogramming central metabolism to minimize byproducts and autonomously supply critical cofactors, thereby eliminating the need for exogenous vitamin B₁₂ through enhanced endogenous synthesis [\\u003cspan citationid=\\\"CR16\\\" class=\\\"CitationRef\\\"\\u003e15\\u003c/span\\u003e]. These advances demonstrate the potential of systematic metabolic engineering. Similarly, the use of low-cost carbon sources such as glucose presents a compelling route for \\u003cem\\u003eL. reuteri\\u003c/em\\u003e, but its potential to boost 3-HPA yield remains largely untapped because of CCR [\\u003cspan citationid=\\\"CR17\\\" class=\\\"CitationRef\\\"\\u003e16\\u003c/span\\u003e].\\u003c/p\\u003e \\u003cp\\u003eIn this study, an integrated approach combining multi-omics technologies and metabolic engineering was used to decipher the carbon source-dependent metabolic trilemma in \\u003cem\\u003eL. reuteri\\u003c/em\\u003e LR618. The metabolic responses to different carbon sources were systematically investigated, revealing that galactose orchestrates a tripartite mechanism: UDP-galactose accumulation alleviates Crc-mediated CCR, pentose phosphate pathway flux rebalances ATP/NADH pools, and transcriptional co-activation of \\u003cem\\u003edhaB\\u003c/em\\u003e (GDHt) and \\u003cem\\u003ecob\\u003c/em\\u003e genes coordinates holoenzyme assembly. Guided by these mechanistic insights, a rationally designed engineering strategy was implemented in a glucose-based fermentation system by combining GDHt overexpression with targeted supplementation of coenzyme B₁₂, ATP, K⁺, and Mg\\u0026sup2;⁺. This approach achieved a 17.34-fold increase in 3-HPA yield (28.96 g/L) compared with the native glucose-based system, substantially exceeding previously reported levels. This work provides a systems-level framework for leveraging native metabolic networks in probiotic chassis and indicates that coordinated multi-target interventions are important for overcoming the metabolic trilemma in sustainable antimicrobial production.\\u003c/p\\u003e\"},{\"header\":\"Materials and Methods\",\"content\":\"\\u003cdiv id=\\\"Sec3\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eBacterial strain and cultivation conditions\\u003c/h2\\u003e \\u003cp\\u003e \\u003cem\\u003eL. reuteri\\u003c/em\\u003e LR618, isolated from pig feed, was used as the experimental strain. To investigate the effects of different carbon sources on strain growth and subsequent 3-hydroxypropionaldehyde (3-HPA) production, pre-cultures were established in de Man-Rogosa-Sharpe (MRS) broth. The medium was supplemented with one of the following sole carbon sources: glucose, lactose, galactose, sucrose, dextrin, soluble starch, maltose, fructose, deoxyribose, xylose, or glycerol, each at 20 g/L, consistent with the carbohydrate concentration in the standard MRS formulation. Static cultures were incubated at 37\\u0026deg;C under 5% CO₂ for 26 h. Growth was monitored by measuring the optical density at 600 nm (\\u003cem\\u003eOD\\u003c/em\\u003e₆₀₀), and cultures were harvested at the stationary phase to ensure a consistent physiological state for subsequent experiments.\\u003c/p\\u003e \\u003c/div\\u003e\\n\\u003ch3\\u003eGlycerol conversion reactions\\u003c/h3\\u003e\\n\\u003cp\\u003eAfter 26 h of cultivation, cells were harvested by centrifugation at 8,000 \\u0026times; g for 10 min at 4\\u0026deg;C. The pellets were washed twice with sterile phosphate-buffered saline (pH 6.0) and resuspended in glycerol conversion medium consisting of 50 mM potassium phosphate buffer (pH 6.0) supplemented with 600 mM glycerol. The final inoculum size was adjusted to a wet cell weight (WCW) of 10 g/L. Bioconversion reactions were carried out in 250 mL Erlenmeyer flasks at 37\\u0026deg;C under static conditions to produce 3-HPA. Samples were collected every 0.5 h. A 5 mL aliquot of the reaction mixture was rapidly chilled on ice to halt metabolic activity and then centrifuged at 12,000 \\u0026times; g for 5 min at 4\\u0026deg;C to separate cells from the supernatant. Supernatants were stored at 4\\u0026deg;C for biochemical analyses to quantify 3-HPA and other metabolites. Cell pellets were frozen in liquid nitrogen and stored at \\u0026minus;\\u0026thinsp;80\\u0026deg;C for metabolomic and transcriptomic profiling.\\u003c/p\\u003e \\u003cp\\u003eThe 3-HPA concentration was determined spectrophotometrically. A standard curve was established using purified 3-HPA (0.05\\u0026ndash;1.5 mg/L) dissolved in ultrapure water. For sample analysis, 1 mL culture supernatant was mixed with 0.75 mL 10 mM tryptophan reagent and 3 mL concentrated HCl, followed by vortexing and incubation at 37\\u0026deg;C for 20 min. Absorbance at 560 nm was measured using a UV-Vis spectrophotometer (Shimadzu, Japan). The 3-HPA concentration was then calculated from the standard curve.\\u003c/p\\u003e\\n\\u003ch3\\u003eMetabolomic profiling of LR618\\u003c/h3\\u003e\\n\\u003cp\\u003eMetabolite extraction from cells: A 100 \\u0026micro;L aliquot of cell pellets was mixed with 400 \\u0026micro;L extraction solution (MeOH:ACN, 1:1, v/v). The extraction solution contained deuterated internal standards. The mixture was vortexed for 30 s, sonicated for 10 min in a 4\\u0026deg;C water bath, and incubated for 1 h at \\u0026minus;\\u0026thinsp;40\\u0026deg;C to precipitate proteins. Samples were then centrifuged at 12,000 rpm for 15 min at 4\\u0026deg;C. The supernatant was transferred to a fresh glass vial for analysis. The quality control sample was prepared by pooling equal aliquots of the supernatants from all samples.\\u003c/p\\u003e \\u003cp\\u003eLC-MS analysis: LC-MS/MS analyses were performed using a UHPLC system (Vanquish, Thermo Fisher Scientific) equipped with a Waters ACQUITY UPLC BEH Amide column (2.1 mm \\u0026times; 50 mm, 1.7 \\u0026micro;m) coupled to an Orbitrap Exploris 120 mass spectrometer (Thermo). The mobile phase consisted of 25 mmol/L ammonium acetate and 25 mmol/L ammonium hydroxide in water (pH\\u0026thinsp;=\\u0026thinsp;9.75) (A) and acetonitrile (B). The autosampler temperature was 4\\u0026deg;C, and the injection volume was 2 \\u0026micro;L. The Orbitrap Exploris 120 mass spectrometer was operated in information-dependent acquisition mode under the control of Xcalibur software (Thermo) to acquire MS/MS spectra. In this mode, the acquisition software continuously evaluates the full-scan MS spectrum. The electrospray ionization source conditions were set as follows: sheath gas flow rate, 50 Arb; auxiliary gas flow rate, 15 Arb; capillary temperature, 320\\u0026deg;C; full MS resolution, 60,000; MS/MS resolution, 15,000; collision energy, stepped normalized collision energy 20/30/40; and spray voltage, 3.8 kV in positive mode and \\u0026minus;\\u0026thinsp;3.4 kV in negative mode.\\u003c/p\\u003e\\n\\u003ch3\\u003eTranscriptomic profiling of LR618\\u003c/h3\\u003e\\n\\u003cp\\u003eTriplicate biological replicates were harvested by rapid centrifugation (8,000 \\u0026times; g, 4\\u0026deg;C, 5 min). Cell pellets were immediately flash-frozen in liquid nitrogen and stored at \\u0026minus;\\u0026thinsp;80\\u0026deg;C until RNA extraction. Total RNA was isolated using the RNeasy Mini Kit (Qiagen, Germany) with on-column DNase I treatment to remove genomic DNA. RNA integrity was verified using an Agilent 2100 Bioanalyzer (RNA Integrity Number, RIN\\u0026thinsp;\\u0026ge;\\u0026thinsp;9.0), and RNA quantity was measured using a Qubit 4.0 Fluorometer (Thermo Fisher Scientific, USA). Ribosomal RNA was depleted using the MICROBExpress\\u0026trade; Bacterial mRNA Enrichment Kit (Ambion, USA) to enrich mRNA. Strand-specific RNA-seq libraries were prepared using the NEBNext\\u0026reg; Ultra\\u0026trade; II Directional RNA Library Prep Kit (NEB, USA) according to the manufacturer\\u0026rsquo;s protocol. Briefly, fragmented RNA (200\\u0026ndash;300 nt) underwent cDNA synthesis, end repair, adapter ligation, and PCR amplification (12 cycles). Library quality was assessed using an Agilent 2100 Bioanalyzer (average insert size, 370\\u0026ndash;420 bp) and quantified by qPCR (Kapa Biosystems, USA). Paired-end sequencing (2 \\u0026times; 150 bp) was performed on the Illumina NovaSeq 6000 platform (Novogene, China). Raw reads were preprocessed using Trimmomatic v0.39 to remove adapters and low-quality bases (Phred score\\u0026thinsp;\\u0026lt;\\u0026thinsp;20). Clean reads were aligned to the \\u003cem\\u003eL. reuteri\\u003c/em\\u003e DSM 17938 reference genome (NCBI Assembly ASM2698v1) using Bowtie2 v2.4.2 with default parameters. Transcript abundance was quantified using feature counts v2.0.3, and differentially expressed genes (DEGs) were identified using DESeq2 v1.34.0 (|log₂FC| \\u0026gt; 2, FDR-adjusted \\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05). Functional annotation of DEGs was performed using the KEGG Orthology (KO) and Gene Ontology (GO) databases through clusterProfiler v4.4.4.\\u003c/p\\u003e\\n\\u003ch3\\u003eValidation of glycerol dehydratase overexpression\\u003c/h3\\u003e\\n\\u003cp\\u003eThe \\u003cem\\u003edhaB\\u003c/em\\u003e operon was amplified from LR618 genomic DNA using primers \\u003cem\\u003eGDHt\\u003c/em\\u003e-F/R under the following PCR conditions: 98\\u0026deg;C for 3 min; 30 cycles of 98\\u0026deg;C for 10 s, 55\\u0026deg;C for 15 s, and 72\\u0026deg;C for 3 min; followed by 72\\u0026deg;C for 5 min. The purified amplicon (TransGen Gel Kit) was cloned into pLEM415 by homologous recombination and electroporated into LR618 at 2.0 kV for 5 ms. The transformant (LR620) was selected on erythromycin (10 \\u0026micro;g/mL). 3-HPA yields of LR620 and the controls, wild-type LR618 and empty-plasmid LR618/pLEM415, were assayed as described above. For qRT-PCR, RNA from the pre-culture (26 h) and bioconversion (2 h) phases was reverse-transcribed, and \\u003cem\\u003edhaB\\u003c/em\\u003e expression was quantified using \\u003cem\\u003eGDHt\\u003c/em\\u003e-Q-F/R primers with 16S ribosomal RNA (16S-Q-F/R) gene as the reference gene. The qPCR program consisted of 40 cycles at 95\\u0026deg;C for 5 s and 60\\u0026deg;C for 30 s. Fold changes were calculated using the 2^\\u0026minus;ΔΔ\\u003cem\\u003eCt\\u003c/em\\u003e method.\\u003c/p\\u003e \\u003cdiv id=\\\"Sec8\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eExogenous metabolite supplementation\\u003c/h2\\u003e \\u003cp\\u003eThe engineered strain LR620 was precultured anaerobically in MRS broth. Single-factor experiments were performed to examine the effects of biotransformation time (0.5\\u0026ndash;5 h), temperature (23\\u0026ndash;51\\u0026deg;C), inoculum size (10\\u0026ndash;130 g/L, WCW), pH (4\\u0026ndash;9), and glycerol concentration (100\\u0026ndash;800 mM). An orthogonal design was then used to evaluate interactions among these factors. Based on the orthogonal analysis, the most significant factors were further optimized by response surface methodology using a Box-Behnken design. To investigate glycerol dehydratase reactivation, exogenous additives were supplemented into the glycerol solution under the optimized conditions. The individual additives tested were KCl (1\\u0026ndash;5%), ATP (0.1\\u0026ndash;0.9%), coenzyme B₁₂ (0.001\\u0026ndash;0.009%), and MgCl₂ (0.03\\u0026ndash;0.15%). Based on the optimal individual concentrations, binary, ternary, and quaternary combinations were then tested. All experiments were performed in triplicate.\\u003c/p\\u003e \\u003c/div\\u003e\\n\\u003ch3\\u003eStatistical analyses\\u003c/h3\\u003e\\n\\u003cp\\u003eAll data in this study represent the mean (\\u0026plusmn;\\u0026thinsp;standard deviation) of three independent experiments. Statistical analyses (\\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05) were performed using SPSS 20.0 to analyze the relationship between metabolomic and transcriptomic data. Origin software was used for graphical analysis.\\u003c/p\\u003e\"},{\"header\":\"Results and Discussion\",\"content\":\"\\u003cdiv id=\\\"Sec11\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eCarbon source screening identifies galactose as superior for 3-HPA production\\u003c/h2\\u003e \\u003cp\\u003eIn the two-step fermentation process using \\u003cem\\u003eL. reuteri\\u003c/em\\u003e LR618, 3-HPA production varied significantly across carbon sources (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e). Galactose supported the highest titer (8.51 g/L), followed by lactose (7.01 g/L), whereas glucose yielded only 1.67 g/L. Deoxyribose (1.37 g/L) and xylose (1.28 g/L) showed moderate production, whereas maltose, dextrin, soluble starch, and sucrose produced negligible amounts (\\u0026lt;\\u0026thinsp;1 g/L). Strikingly, growth curve analysis revealed that glucose supported rapid proliferation, with biomass reaching \\u003cem\\u003eOD\\u003c/em\\u003e\\u003csub\\u003e600\\u003c/sub\\u003e 5.1, whereas galactose-cultured cells grew slowly, attaining a maximum \\u003cem\\u003eOD\\u003c/em\\u003e\\u003csub\\u003e600\\u003c/sub\\u003e of only 3.8. This dramatic disparity suggests fundamental differences in how these carbon sources prime cellular metabolism for subsequent glycerol bioconversion. Galactose and lactose, both metabolized through the Leloir pathway [\\u003cspan citationid=\\\"CR18\\\" class=\\\"CitationRef\\\"\\u003e17\\u003c/span\\u003e], appear to enhance the metabolic capacity for 3-HPA synthesis, whereas glucose and fructose, which preferentially enter glycolysis, do not establish a similarly favorable state. Polysaccharides (starch and dextrin) and disaccharides (sucrose and maltose) likely suffer from inefficient hydrolysis or transport under the experimental conditions, limiting their utility[\\u003cspan citationid=\\\"CR19\\\" class=\\\"CitationRef\\\"\\u003e18\\u003c/span\\u003e]. Based on these results, galactose and glucose were selected as representative high-performance and baseline carbon sources, respectively, for subsequent multi-omics dissection of the underlying mechanisms.\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec12\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eMetabolomic profiling reveals carbon source-dependent metabolic reprogramming\\u003c/h2\\u003e \\u003cp\\u003eTo dissect the metabolic basis of the differential 3-HPA production, untargeted metabolomics was performed on samples from four experimental conditions: cells cultured in glucose for 26 h (PLM26), glucose-cultured cells transferred to glycerol for 2 h (PLG2), cells cultured in galactose for 26 h (BAM26), and galactose-cultured cells transferred to glycerol for 2 h (BAG2). This design enabled separate interrogation of the pre-culture phase (carbon source priming) and the bioconversion phase (3-HPA production).\\u003c/p\\u003e \\u003cp\\u003eMetabolomic profiling revealed 1554 differentially abundant metabolites in the pre-culture phase (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003eA) and 1650 in the bioconversion phase (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003eB). Principal component analysis revealed clear separation between the glucose- and galactose-fed groups, indicating substantial metabolic reprogramming induced by carbon source selection. Pathway enrichment analysis identified coenzyme B₁₂ biosynthesis, the Leloir pathway, glycerol metabolism, and redox homeostasis as the key pathways differentiating the two carbon source regimes. The findings indicate that galactose enhances cofactor synthesis and stress resilience in lactic acid bacteria, whereas glucose-driven glycolytic overflow is associated with redox imbalance.\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003cp\\u003eIn the pre-culture phase, the most significantly enriched pathways were concentrated in amino acid metabolism (alanine, aspartate and glutamate metabolism, beta-alanine metabolism), cofactor and vitamin metabolism (nicotinate and nicotinamide metabolism, pantothenate and CoA biosynthesis), with the above pathways showing high enrichment significance (\\u003cem\\u003eP\\u003c/em\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05) and abundant differential molecules (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003eA). In galactose-preconditioned cells (BAM26), metabolic priming was characterized by synergistic upregulation of the Leloir pathway and coenzyme B₁₂ biosynthesis. Elevated levels of UDP-galactose, a key Leloir intermediate, were observed (2.3-fold vs. PLM26, \\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.01), accompanied by accumulation of 5\\u0026prime;-deoxyadenosylcobalamin, a coenzyme B₁₂ precursor. Concurrent enrichment of the methionine salvage and selenocompound metabolism pathways supplied ATP and selenocysteine, both essential for coenzyme B₁₂ synthesis. This coordinated metabolic configuration ensured that, upon transfer to glycerol, both GDHt and its essential cofactor were readily available [\\u003cspan citationid=\\\"CR20\\\" class=\\\"CitationRef\\\"\\u003e19\\u003c/span\\u003e].\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003cp\\u003eIn contrast, glucose-preconditioned cells (PLM26) exhibited metabolic suppression characterized by accumulation of acidic byproducts (acetate and lactate) and depletion of nucleotide sugars. This acidification may trigger stress responses that downregulate nonessential pathways, whereas CCR mediated by the Crc system actively silences genes involved in alternative carbon utilization [\\u003cspan citationid=\\\"CR21\\\" class=\\\"CitationRef\\\"\\u003e20\\u003c/span\\u003e]. The absence of UDP-galactose and coenzyme B₁₂ precursors in PLM26 indicates that glucose metabolism fails to establish the metabolic infrastructure required for efficient 3-HPA synthesis.\\u003c/p\\u003e \\u003cp\\u003eIn sharp contrast, the strain exhibited profound metabolic network remodeling during glycerol bioconversion for 3-HPA production, with core enriched pathways shifting to those directly associated with 3-HPA biosynthesis\\u0026mdash;alanine, aspartate and glutamate metabolism remained highly significant, alongside robust enrichment of glycerol catabolism, coenzyme regeneration and energy supply pathways, and sustained significant enrichment of stress resistance-related pathways (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003eB). Upon transfer to glycerol, galactose-fed cells (BAG2) achieved 5.1-fold higher 3-HPA titers than glucose-fed cells (PLG2), driven by different metabolic configurations. In BAG2, glycerol was preferentially channeled through the reductive pathway, as indicated by elevated GDHt activity markers and reduced glycerol-3-phosphate accumulation (1.2-fold downregulation of glycerol kinase flux). Critically, redox analysis revealed a more oxidative NAD⁺/NADH ratio in BAG2 (2.3) than in PLG2 (1.6, \\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05), which suppressed PDOR-mediated diversion of 3-HPA to 1,3-propanediol. This favorable redox state originated from Leloir pathway activity during pre-culture, which generates NADH in a controlled manner without the glycolytic overflow characteristic of glucose metabolism. Glucose-fed cells (PLG2) displayed metabolic dysregulation characterized by ATP depletion and NADH overload. Excess NADH activated PDOR, redirecting 3-HPA toward 1,3-propanediol as a redox sink. At the same time, acid stress caused by organic acids produced during pre-culture persisted and further compromised metabolic efficiency [\\u003cspan citationid=\\\"CR22\\\" class=\\\"CitationRef\\\"\\u003e21\\u003c/span\\u003e].\\u003c/p\\u003e \\u003cp\\u003eUDP-galactose, a key intermediate of the Leloir pathway, accumulated to 2.3-fold higher levels in galactose-fed cells than in glucose-fed controls (\\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.01). Similarly, S-adenosylmethionine (SAM) reserves were 2.1-fold higher in galactose-preconditioned cells (\\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05) and persisted during bioconversion. The NAD⁺/NADH ratio was also significantly elevated in galactose-fed bioconversion cultures (2.3) compared with glucose-fed controls (1.6, \\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05). These metabolite signatures\\u0026mdash;UDP-galactose accumulation, SAM enrichment, and a more oxidized redox state\\u0026mdash;collectively distinguished the metabolic configuration established by galactose from that of glucose. The mechanistic implications of these metabolic differences are further explored through transcriptomic analysis in the following section.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec13\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eTranscriptomic reprogramming of glycerol metabolism by galactose\\u003c/h2\\u003e \\u003cp\\u003eTo complement the metabolomic findings and establish the transcriptional basis of the metabolic advantages conferred by galactose, comparative transcriptomics was performed on the same four experimental conditions. RNA-seq analysis identified 346 differentially expressed genes between the galactose- and glucose-fed groups (|log₂FC| \\u0026gt; 1, FDR-adjusted \\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05), Hierarchical clustering showed clear transcriptional specialization under galactose conditions, with BAM26 and BAG2 forming a cohesive cluster. Transcriptomic profiling revealed 374 differentially expressed metabolism-associated transcripts across the experimental conditions in the pre-culture phase and 331 in the bioconversion phase (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig4\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003eA, B).\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003cp\\u003eDuring the bioconversion phase, galactose feeding resulted in 3.1-fold upregulation of GDHt (EC 4.2.1.30) gene in BAG2 relative to PLG2 (\\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05) (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig5\\\" class=\\\"InternalRef\\\"\\u003e5\\u003c/span\\u003e). This transcriptional elevation directly correlated with the enhanced 3-HPA synthesis capacity observed in the metabolomic analysis. Importantly, this upregulation was not limited to GDHt alone. Genes encoding enzymes required for coenzyme B₁₂ biosynthesis were also co-induced during the pre-culture phase, with \\u003cem\\u003ecobA\\u003c/em\\u003e (C6H63_RS00365) and \\u003cem\\u003ecobQ\\u003c/em\\u003e (C6H63_RS00370) upregulated 2.6- and 3.3-fold, respectively, in BAM26 relative to PLM26 (\\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05). This coordinated transcriptional program indicates that glycerol exposure significantly enhances the availability of both the GDHt and its essential cofactor coenzyme B₁₂, a key regulatory axis driving glycerol-dependent 3-HPA biosynthesis in \\u003cem\\u003eLimosilactobacillus reuteri\\u003c/em\\u003e that was comprehensively characterized in the previous work [\\u003cspan citationid=\\\"CR23\\\" class=\\\"CitationRef\\\"\\u003e22\\u003c/span\\u003e].\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003cp\\u003eGalactose metabolism was driven by 4.2-fold induction of Leloir pathway genes (\\u003cem\\u003egalK\\u003c/em\\u003e, C6H63_RS00285; \\u003cem\\u003egalT\\u003c/em\\u003e, C6H63_RS00290), enabling efficient galactose catabolism to generate ATP and NADH without the glycolytic overflow characteristic of glucose metabolism. At the same time, genes encoding the oxidative glycerol pathway (\\u003cem\\u003eglpK\\u003c/em\\u003e, C6H63_RS00450; \\u003cem\\u003eglpD\\u003c/em\\u003e, C6H63_RS00455) were downregulated 2.5-fold in galactose-fed cells, thereby minimizing carbon diversion toward glycolysis and ensuring that glycerol entering the cell was preferentially directed toward 3-HPA synthesis. This transcriptional configuration, activation of the desired pathway coupled with suppression of competing routes, represents a more favorable metabolic state that glucose metabolism fails to establish.\\u003c/p\\u003e \\u003cp\\u003eThe co-upregulation of \\u003cem\\u003edhaB\\u003c/em\\u003e and \\u003cem\\u003ecob\\u003c/em\\u003e genes suggests a transcriptionally coupled mechanism that optimizes GDHt function. This dual regulation differs from previous \\u003cem\\u003eL. reuteri\\u003c/em\\u003e studies in which coenzyme B₁₂ limitation constrained GDHt activity despite adequate enzyme expression [\\u003cspan citationid=\\\"CR24\\\" class=\\\"CitationRef\\\"\\u003e23\\u003c/span\\u003e], highlighting the unique ability of galactose to bypass this bottleneck. The transcriptional coupling likely arises from shared regulatory elements responsive to UDP-galactose or related metabolites, thereby linking carbon source availability to both pathway enzyme production and cofactor synthesis.\\u003c/p\\u003e \\u003cp\\u003eCCR in \\u003cem\\u003eL. reuteri\\u003c/em\\u003e is mediated by the Crc system, which actively represses genes related to alternative carbon utilization in glucose-grown cells. In galactose-fed cells, \\u003cem\\u003ecrc\\u003c/em\\u003e transcript levels were reduced 1.5-fold (\\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05), consistent with alleviation of CCR. The accumulation of UDP-galactose, which does not occur during glucose metabolism, correlated with this derepression, suggesting that this metabolite or one of its derivatives directly or indirectly modulates Crc activity (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig6\\\" class=\\\"InternalRef\\\"\\u003e6\\u003c/span\\u003e). This UDP-galactose-Crc regulatory axis represents a previously unreported mechanism linking carbon source identity to pathway activation in lactic acid bacteria.\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003cp\\u003eThe transcriptomic data provide a mechanistic explanation for the metabolomic observations. The elevated GDHt activity inferred from metabolite profiles in BAG2 directly reflects transcriptional upregulation of \\u003cem\\u003edhaB\\u003c/em\\u003e. The favorable NAD⁺/NADH ratio in galactose-fed cells arises from induction of the Leloir pathway, which generates NADH without the excessive glycolytic flux characteristic of glucose metabolism. Sustained coenzyme B₁₂ availability during bioconversion results from induction of \\u003cem\\u003ecob\\u003c/em\\u003e genes during pre-culture, combined with SAM-mediated stabilization. Together, these data indicate that galactose orchestrates a multilayered metabolic program, transcriptional, enzymatic, and metabolic, that collectively optimizes 3-HPA synthesis.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec14\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eHierarchical metabolic constraints govern 3-HPA biosynthetic efficiency\\u003c/h2\\u003e \\u003cp\\u003eThe multi-omics framework described above predicted that efficient 3-HPA production requires coordinated resolution of all three components of the metabolic trilemma: coenzyme B₁₂ availability, redox balance, and transcriptional derepression. To test this prediction and translate mechanistic insights into engineering strategies, systematic phenotypic analyses were performed across engineered and supplemented \\u003cem\\u003eL. reuteri\\u003c/em\\u003e strains. To validate the rate-limiting role of GDHt predicted by transcriptomics, strain LR620 was constructed by overexpressing the \\u003cem\\u003edhaB\\u003c/em\\u003e operon in LR618 using plasmid pLEM415. qRT-PCR confirmed 4.5-fold upregulation of \\u003cem\\u003edhaB\\u003c/em\\u003e in LR620 relative to the wild type (\\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05) (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig7\\\" class=\\\"InternalRef\\\"\\u003e7\\u003c/span\\u003eA). In the glucose-based two-step fermentation system, LR620 produced 5.77 g/L 3-HPA, a 3.45-fold increase over wild-type LR618 (1.67 g/L, \\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05), confirming GDHt as a key bottleneck (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig7\\\" class=\\\"InternalRef\\\"\\u003e7\\u003c/span\\u003eB).\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003cp\\u003eHowever, two observations indicated that GDHt overexpression alone was insufficient to achieve galactose-level performance. First, the empty-plasmid control (LR618/pLEM415) yielded only 1.42 g/L, which was 15% lower than the wild type (\\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05), directly demonstrating a measurable ATP burden caused by plasmid maintenance. This 15% deficit is consistent with the metabolomic observation of energy depletion in glucose-fed systems, in which ATP scarcity constrains multiple metabolic processes. Second, despite elevated GDHt expression, LR620 showed no increase in endogenous coenzyme B₁₂ synthesis, with qRT-PCR showing unchanged \\u003cem\\u003ecobA\\u003c/em\\u003e expression relative to the wild type (1.02-fold, \\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05). This uncoupling of enzyme overexpression from cofactor biosynthesis, a feature inherent to galactose metabolism but absent in glucose-based engineering, explains why the LR620 yield (5.77 g/L) remained below that achieved with galactose (8.51 g/L) despite higher GDHt transcript levels [\\u003cspan citationid=\\\"CR25\\\" class=\\\"CitationRef\\\"\\u003e24\\u003c/span\\u003e].\\u003c/p\\u003e \\u003cp\\u003eAfter establishing that enzyme availability is necessary but insufficient, systematic optimization of biotransformation parameters was carried out using LR620. Initial single-factor experiments established the baseline ranges of biotransformation time (0.5\\u0026ndash;5 h), temperature (23\\u0026ndash;51\\u0026deg;C), inoculum size (10\\u0026ndash;130 g/L, WCW), pH (4\\u0026ndash;9), and glycerol concentration (100\\u0026ndash;800 mM). To capture interactions among factors, an L₁₆(3⁵) orthogonal design was implemented. ANOVA revealed that inoculum size and glycerol concentration exerted the strongest effects on 3-HPA production, followed by temperature and pH, whereas biotransformation time within the 1.5\\u0026ndash;3.0 h range was not significant. The optimal combination identified by range analysis, 110 g/L inoculum size, 700 mM glycerol, 30\\u0026deg;C, pH 6.0, and 2.0 h, increased the titer to 18.54 g/L.\\u003c/p\\u003e \\u003cp\\u003eResponse surface methodology using a Box-Behnken design was then applied to refine the three most significant factors: inoculum size, glycerol concentration, and temperature. The quadratic model showed an excellent fit (R\\u0026sup2; = 0.9957, adjusted R\\u0026sup2; = 0.9901) and a non-significant lack of fit (\\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05), validating its predictive capacity. Model coefficients revealed significant positive linear effects of inoculum size (\\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05) and glycerol concentration (\\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05), as well as significant negative quadratic effects for all three factors (\\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05), indicating the existence of optimal intermediate values. The interaction term inoculum size with temperature was significant (\\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05), demonstrating that the effect of inoculum size depends on temperature, likely reflecting a trade-off between enzyme availability at high cell density and temperature-sensitive mass transfer efficiency. The model predicted a maximum 3-HPA production of 23.5 g/L at 102 g/L inoculum size (WCW), 724 mM glycerol, and 28.9\\u0026deg;C. Experimental validation under these conditions yielded 22.79 g/L, closely matching the prediction and representing a 3.95-fold improvement over the initial unoptimized condition (5.77 g/L for LR620 without optimization). This progression, from 5.77 to 18.54 to 22.79 g/L, demonstrates the value of hierarchical optimization that progressively incorporates additional layers of system complexity.\\u003c/p\\u003e \\u003cp\\u003eDespite achieving 22.79 g/L through parameter optimization, 3-HPA accumulation remained transient, peaking at 2.0 h and declining thereafter. This pattern reflects the well-characterized suicide inactivation of GDHt. Upon binding glycerol, the enzyme induces a conformational change in coenzyme B₁₂, leading to irreversible cofactor-enzyme complexation and loss of catalytic activity [\\u003cspan citationid=\\\"CR26\\\" class=\\\"CitationRef\\\"\\u003e25\\u003c/span\\u003e]. Reactivation requires removal of the damaged cofactor and replacement with fresh coenzyme B₁₂, an ATP-dependent process facilitated by Mg\\u0026sup2;⁺ ions, whereas K⁺ is essential for maintaining active-site conformation and substrate positioning [\\u003cspan citationid=\\\"CR27\\\" class=\\\"CitationRef\\\"\\u003e26\\u003c/span\\u003e].\\u003c/p\\u003e \\u003cp\\u003eTo overcome this limitation, exogenous additives were tested individually under the optimized biotransformation conditions. KCl supplementation, as a K⁺ source, enhanced 3-HPA production in a dose-dependent manner, with an optimum at 3% (w/v), yielding 24.33 g/L, a 6.76% increase over the response surface methodology-optimized baseline (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig8\\\" class=\\\"InternalRef\\\"\\u003e8\\u003c/span\\u003eA). Exogenous ATP supplementation was investigated to directly address the energy demand associated with GDHt reactivation. ATP showed a clear parabolic effect (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig8\\\" class=\\\"InternalRef\\\"\\u003e8\\u003c/span\\u003eB), with 3-HPA production peaking at 0.3% ATP (25.11 g/L, 10% above baseline) and declining at higher concentrations. Mechanistically, low ATP concentrations may promote GDHt reactivation by driving ejection of damaged coenzyme B₁₂ [\\u003cspan citationid=\\\"CR28\\\" class=\\\"CitationRef\\\"\\u003e27\\u003c/span\\u003e], whereas excess ATP may inhibit GAPDH and disrupt energy homeostasis [\\u003cspan citationid=\\\"CR29\\\" class=\\\"CitationRef\\\"\\u003e28\\u003c/span\\u003e], thereby redirecting carbon flux away from glycerol assimilation. These findings confirm the energy limitation inferred from the 15% plasmid burden effect and indicate that ATP improves production only within a narrow optimal range.\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003cp\\u003eCoenzyme B₁₂ supplementation was more effective. Titers increased progressively to 25.56 g/L at 0.005% (w/v), representing a 12% improvement (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig8\\\" class=\\\"InternalRef\\\"\\u003e8\\u003c/span\\u003eC). Notably, the response was also parabolic, because concentrations above 0.005% (0.007\\u0026ndash;0.009%) led to progressively lower titers, suggesting that supra-optimal B₁₂ may compete with native cofactor for binding without contributing to productive catalysis. MgCl₂ supplementation showed a narrower optimum, with peak production (23.21 g/L) at 0.06% (w/v) and sharp declines at higher concentrations, likely because of osmotic stress (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig8\\\" class=\\\"InternalRef\\\"\\u003e8\\u003c/span\\u003eD). Based on the optimal individual concentrations of each component, various combinatorial supplementations were then investigated to identify potential synergistic effects. The quaternary combination (ATP\\u0026thinsp;+\\u0026thinsp;KCl\\u0026thinsp;+\\u0026thinsp;B₁₂ + MgCl₂) produced the highest titer, 28.96 g/L, corresponding to a 27% improvement over the response surface methodology-optimized baseline (22.79 g/L) and a 17.34-fold increase over the initial unoptimized condition (1.67 g/L for wild-type LR618) (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003e \\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab1\\\" border=\\\"1\\\"\\u003e \\u003ccaption language=\\\"En\\\"\\u003e \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 1\\u003c/div\\u003e \\u003cdiv class=\\\"CaptionContent\\\"\\u003e \\u003cp\\u003e3-HPA Production of \\u003cem\\u003eLimosilactobacillus reuteri\\u003c/em\\u003e under Different Treatments.\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"3\\\"\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eRegion\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eTreatments\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e3-HPA\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eLR618\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eglucose as the carbon source\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1.67 g/L\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eLR618\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003egalactose as the carbon source\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e8.51 g/L\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eLR618/pLEM415\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003ewith the pLEM415\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1.42 g/L\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eLR620\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eoverexpression of GDHt\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e5.77 g/L\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eLR620\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eoptimization of fermentation engineering\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e22.79 g/L\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eLR620\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003ewith 0.3% ATP\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e25.11 g/L\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eLR620\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003ewith 3% KCl\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e24.33 g/L\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eLR620\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003ewith 0.005% coenzyme B₁₂\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e25.56 g/L\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eLR620\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003ewith 0.06% MgCl₂\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e23.21 g/L\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eLR620\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003ewith ATP, KCl, coenzyme B₁₂, MgCl₂\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e28.96 g/L\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e \\u003cp\\u003eMechanistically, these results establish a hierarchical framework for GDHt function. The reactivation process itself, removal of damaged B₁₂ and replacement with fresh cofactor, requires ATP and Mg\\u0026sup2;⁺ and is enabled by exogenous B₁₂ supplementation. However, once reactivated, the enzyme must rapidly bind glycerol and convert it to 3-HPA, a catalytic step critically dependent on K⁺ for active-site integrity. Thus, K⁺ acts as a gatekeeper that translates reactivation events into productive catalysis [\\u003cspan citationid=\\\"CR30\\\" class=\\\"CitationRef\\\"\\u003e29\\u003c/span\\u003e]. In its absence, even successfully reactivated enzyme molecules show suboptimal activity, thereby limiting total 3-HPA production. The additive concentrations (3% KCl, 0.3% ATP, 0.005% B₁₂, 0.06% MgCl₂) represent economically viable levels compatible with downstream processing.\\u003c/p\\u003e \\u003cp\\u003eThe systematic phenotypic analyses provide quantitative validation of the multi-omics framework and explain why single-gene engineering alone cannot achieve galactose-level performance. The performance gap between LR620 (22.79 g/L after process optimization) and the fully supplemented system (28.96 g/L) reflects three fundamental limitations. First, cellular energy status imposes a measurable constraint. The empty-plasmid control produced 15% less 3-HPA than wild-type LR618 (1.42 vs. 1.67 g/L), directly demonstrating that ATP diversion to plasmid replication and selection marker expression restricts energy-intensive processes. The parabolic response to exogenous ATP supplementation, peak at 0.3% (25.11 g/L) and decline at \\u0026ge;\\u0026thinsp;0.5%, further shows that energy availability must be carefully balanced. Insufficient ATP limits GDHt reactivation, whereas excess ATP may inhibit GAPDH and disrupt central metabolism [\\u003cspan citationid=\\\"CR29\\\" class=\\\"CitationRef\\\"\\u003e28\\u003c/span\\u003e]. This narrow optimal range explains why ATP supplementation alone, without coordinated optimization of cofactors and ions, cannot achieve maximal yields.\\u003c/p\\u003e \\u003cp\\u003eSecond, enzyme overexpression without cofactor enhancement creates an unbalanced metabolic state. Despite 4.5-fold higher \\u003cem\\u003edhaB\\u003c/em\\u003e expression, LR620 showed no increase in endogenous coenzyme B₁₂ synthesis, as \\u003cem\\u003ecobA\\u003c/em\\u003e expression remained unchanged. This uncoupling means that, although more GDHt apoenzyme is available, the supply of its essential cofactor remains limiting. This is a classic example of hierarchical metabolic constraint in which downstream capacity cannot compensate for an upstream limitation [\\u003cspan citationid=\\\"CR31\\\" class=\\\"CitationRef\\\"\\u003e30\\u003c/span\\u003e]. The 3.45-fold improvement from GDHt overexpression (5.77 vs. 1.67 g/L) was therefore substantially lower than the 5.1-fold advantage achieved through galactose\\u0026rsquo;s coordinated program (8.51 vs. 1.67 g/L).\\u003c/p\\u003e \\u003cp\\u003eThird, the ionic and cofactor environment imposes its own constraints. The parabolic responses to exogenous coenzyme B₁₂ and ATP, the narrow optimal range for Mg\\u0026sup2;⁺, and the indispensable role of K⁺ in translating reactivation into catalysis (detailed in Section 3.4.3) collectively reveal that each component of the GDHt functional cycle must be precisely balanced. Together with the ATP burden from plasmid maintenance and the uncoupling of enzyme overexpression from cofactor synthesis, these observations establish that hierarchical metabolic constraints\\u0026mdash;spanning energy supply, cofactor availability, and ionic environment\\u0026mdash;cannot be overcome by single-gene engineering alone [\\u003cspan citationid=\\\"CR31\\\" class=\\\"CitationRef\\\"\\u003e30\\u003c/span\\u003e].\\u003c/p\\u003e \\u003cp\\u003eFinally, The multi-omics analyses and phenotypic validations presented above collectively demonstrate that galactose\\u0026rsquo;s native superiority arises from an integrated regulatory program that simultaneously addresses coenzyme B₁₂ availability, redox balance, and carbon catabolite repression. This coordinated tripartite mechanism achieves what reductionist engineering strategies cannot: a self-reinforcing metabolic state in which enzyme supply, cofactor availability, and ionic environment are optimized concurrently. These findings shift the paradigm of microbial aldehyde production away from an \\u0026ldquo;enzyme-centric\\u0026rdquo; approach\\u0026mdash;focused on overexpressing a single rate-limiting step\\u0026mdash;toward a \\u0026ldquo;systems-centric\\u0026rdquo; framework in which cofactor dynamics, energy management, and ionic homeostasis dominate yield optimization. Future efforts should prioritize chromosomal integration of GDHt-\\u003cem\\u003ecobA\\u003c/em\\u003e operons coupled with redox-balancing modules, such as NADH oxidase expression or Leloir pathway reconstruction in glucose-grown cells, to eliminate plasmid burden and recapitulate the integrated advantages of galactose in cost-effective glucose-based processes.\\u003c/p\\u003e \\u003c/div\\u003e\"},{\"header\":\"Conclusion\",\"content\":\"\\u003cp\\u003eThis study elucidates a previously unreported cross-phase regulatory axis in \\u003cem\\u003eL. reuteri\\u003c/em\\u003e, in which galactose optimizes 3-HPA biosynthesis through an integrated tripartite mechanism that alleviates CCR, balances ATP/NADH pools, and coordinates holoenzyme assembly\\u0026mdash;together achieving a 5.1-fold increase over glucose-grown cultures. Translating these mechanistic insights into engineering strategies demonstrated that single-gene overexpression alone cannot overcome hierarchical metabolic constraints. Instead, synergistic supplementation with ATP, K⁺, coenzyme B₁₂, and Mg\\u0026sup2;⁺, which addresses enzyme reactivation, cofactor availability, and catalytic efficiency, enabled a 17.34-fold improvement, reaching a final titer of 28.96 g/L. To our knowledge, this is the highest 3-HPA titer reported in \\u003cem\\u003eLimosilactobacillus reuteri\\u003c/em\\u003e in shake-flask fermentation to date. The final optimized process consisted of cell harvest at 24 h, biotransformation at 28.9\\u0026deg;C for 2.0 h with 102 g/L inoculum size (WCW) and 724 mM glycerol (pH 6.0), and supplementation with 3% KCl, 0.3% ATP, 0.005% coenzyme B₁₂, and 0.06% MgCl₂. While achieved at shake-flask scale, this titer provides a strong foundation for bioreactor scale-up studies. This work establishes a systems-level blueprint for reprogramming probiotic chassis and demonstrates that resolving the metabolic trilemma of cofactor dependence, redox imbalance, and CCR is essential for next-generation sustainable antimicrobial production.\\u003c/p\\u003e\"},{\"header\":\"Abbreviations\",\"content\":\"\\u003cdiv class=\\\"DefinitionList\\\"\\u003e \\u003cdiv class=\\\"DefinitionListEntry\\\"\\u003e \\u003cdiv class=\\\"Term\\\"\\u003e3-HPA\\u003c/div\\u003e \\u003cdiv class=\\\"Description\\\"\\u003e \\u003cp\\u003e3-hydroxypropionaldehyde\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/div\\u003e \\u003cdiv class=\\\"DefinitionListEntry\\\"\\u003e \\u003cdiv class=\\\"Term\\\"\\u003eGDHt\\u003c/div\\u003e \\u003cdiv class=\\\"Description\\\"\\u003e \\u003cp\\u003eglycerol dehydratase\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/div\\u003e \\u003cdiv class=\\\"DefinitionListEntry\\\"\\u003e \\u003cdiv class=\\\"Term\\\"\\u003eCCR\\u003c/div\\u003e \\u003cdiv class=\\\"Description\\\"\\u003e \\u003cp\\u003ecarbon catabolite repression\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/div\\u003e \\u003cdiv class=\\\"DefinitionListEntry\\\"\\u003e \\u003cdiv class=\\\"Term\\\"\\u003eqRT‒PCR\\u003c/div\\u003e \\u003cdiv class=\\\"Description\\\"\\u003e \\u003cp\\u003equantitative real‑time PCR\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/div\\u003e \\u003cdiv class=\\\"DefinitionListEntry\\\"\\u003e \\u003cdiv class=\\\"Term\\\"\\u003eWCW\\u003c/div\\u003e \\u003cdiv class=\\\"Description\\\"\\u003e \\u003cp\\u003ewet cell weight\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/div\\u003e \\u003cdiv class=\\\"DefinitionListEntry\\\"\\u003e \\u003cdiv class=\\\"Term\\\"\\u003eMRS\\u003c/div\\u003e \\u003cdiv class=\\\"Description\\\"\\u003e \\u003cp\\u003eMan-Rogosa-Sharpe\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/div\\u003e \\u003cdiv class=\\\"DefinitionListEntry\\\"\\u003e \\u003cdiv class=\\\"Term\\\"\\u003ePDOR\\u003c/div\\u003e \\u003cdiv class=\\\"Description\\\"\\u003e \\u003cp\\u003epropanediol oxidoreductase\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/div\\u003e \\u003cdiv class=\\\"DefinitionListEntry\\\"\\u003e \\u003cdiv class=\\\"Term\\\"\\u003eSAM\\u003c/div\\u003e \\u003cdiv class=\\\"Description\\\"\\u003e \\u003cp\\u003eS-adenosylmethionine\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/div\\u003e \\u003c/div\\u003e\"},{\"header\":\"Declarations\",\"content\":\"\\u003cp\\u003e\\u003cstrong\\u003eEthics approval and consent to participate\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eNot applicable.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eConsent for publication\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eNot applicable.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eAvailability of data and materials\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eAll data generated or analyzed during this study are included in this published article.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eCompeting interests\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe authors declare that they have no competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eFunding\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThis publication was supported by the Project of Science and Technology Innovation Team in Anhui Academy of Agricultural Sciences (No. 2025YL079).\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eAuthors’ contributions\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eQiang Yin designed the project, carried out experiments, and drafted the manuscript.\\u0026nbsp;Liang Li, Yong Fang and FuQiang Chengcarried out experiments. Hang Wu revised the manuscript. Zuojun Liu and Ting Guo supervised the project and revised the manuscript. All authors read and approved the final manuscript.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eDeclaration of competing interest\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe authors declare no conflict of interest.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eAcknowledgments\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eNot applicable.\\u003c/p\\u003e\"},{\"header\":\"References\",\"content\":\"\\u003col\\u003e\\n\\u003cli\\u003eStevens MJ, Vollenweider S, Meile L, Lacroix C: 1,3-Propanediol dehydrogenases in \\u003cem\\u003eLactobacillus reuteri\\u003c/em\\u003e: impact on central metabolism and 3-hydroxypropionaldehyde production. 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Front Med (Lausanne). 2022;9:817957. doi: 10.3389/fmed.2022.817957. \\u003c/li\\u003e\\n\\u003cli\\u003eWang J, Yin Q, Bai H, Wang W, Chen Y, Zhou M, Zhang R, Ding G, Xu Z, Zhang Y: Transcriptome Analysis of Glycerin Regulating Reuterin Production of \\u003cem\\u003eLactobacillus reuteri\\u003c/em\\u003e. Microorganisms. 2023;11(8):2007. doi: 10.3390/microorganisms11082007.\\u003c/li\\u003e\\n\\u003cli\\u003eJu JH, Wang D, Heo SY, Kim MS, Seo JW, Kim YM, Kim DH, Kang SA, Kim CH, Oh BR: Enhancement of 1,3-propanediol production from industrial by-product by \\u003cem\\u003eLactobacillus reuteri\\u003c/em\\u003e CH53. Microb Cell Fact. 2020;19(1):6. doi: 10.1186/s12934-019-1275-x.\\u003c/li\\u003e\\n\\u003cli\\u003eDurrant C, Fuehring JI, Willemetz A, Chr\\u0026eacute;tien D, Sala G, Ghidoni R, Katz A, R\\u0026ouml;tig A, Thelestam M, Ermonval M, Moore SHE: Defects in galactose metabolism and glycoconjugate biosynthesis in a udp-glucose pyrophosphorylase-deficient cell line are reversed by adding galactose to the growth medium. Int J Mol Sci. 2020 ;21(6):2028. doi: 10.3390/ijms21062028.\\u003c/li\\u003e\\n\\u003cli\\u003eYamanishi M, Yunoki M, Tobimatsu T, Sato H, Matsui J, Dokiya A, Iuchi Y, Oe K, Suto K, Shibata N, Morimoto Y, Yasuoka N, Toraya T: The crystal structure of coenzyme B12-dependent glycerol dehydratase in complex with cobalamin and propane-1,2-diol. Eur J Biochem. 2002 ;269(18):4484-94. doi: 10.1046/j.1432-1033.2002.03151.x.\\u003c/li\\u003e\\n\\u003cli\\u003eKajiura H, Mori K, Tobimatsu T, Toraya T: Characterization and mechanism of action of a reactivating factor for adenosylcobalamin-dependent glycerol dehydratase. J Biol Chem. 2001;276(39):36514-9. doi: 10.1074/jbc.M105182200.\\u003c/li\\u003e\\n\\u003cli\\u003eTal N, Ovcharenko E, Lewinson O: A single intact ATPase site of the ABC transporter BtuCD drives 5% transport activity yet supports full in vivo vitamin B12 utilization. Proc Natl Acad Sci U S A. 2013;110(14):5434-9. doi: 10.1073/pnas.1209644110. \\u003c/li\\u003e\\n\\u003cli\\u003eSuzuki H, Makiyama YN, Watanabe Y, Akutsu H, Tajiri M, Motoda Y, Akagi KI, Konuma T, Akashi S, Ikegami T: Analysis of the high-order conformational changes in glyceraldehyde-3-phosphate dehydrogenase induced by nicotinamide adenine dinucleotide, adenosine triphosphate, and oxidants. Biochemistry. 2025 ;64(9):1916-1932. doi: 10.1021/acs.biochem.4c00794.\\u003c/li\\u003e\\n\\u003cli\\u003eYamanishi M, Yunoki M, Tobimatsu T, Sato H, Matsui J, Dokiya A, Iuchi Y, Oe K, Suto K, Shibata N, Morimoto Y, Yasuoka N, Toraya T: The crystal structure of coenzyme B12-dependent glycerol dehydratase in complex with cobalamin and propane-1,2-diol. Eur J Biochem. 2002;269(18):4484-94. doi: 10.1046/j.1432-1033.2002.03151.x. \\u003c/li\\u003e\\n\\u003cli\\u003eRazaghi-Moghadam Z, Nikoloski Z. GeneReg: A constraint-based approach for design of feasible metabolic engineering strategies at the gene level. Bioinformatics. 2021;37(12):1717-1723. doi: 10.1093/bioinformatics/btaa996. \\u003c/li\\u003e\\n\\u003c/ol\\u003e\"}],\"fulltextSource\":\"\",\"fullText\":\"\",\"funders\":[],\"hasAdminPriorityOnWorkflow\":false,\"hasManuscriptDocX\":true,\"hasOptedInToPreprint\":true,\"hasPassedJournalQc\":\"\",\"hasAnyPriority\":false,\"hideJournal\":false,\"highlight\":\"\",\"institution\":\"\",\"isAcceptedByJournal\":false,\"isAuthorSuppliedPdf\":false,\"isDeskRejected\":\"\",\"isHiddenFromSearch\":false,\"isInQc\":false,\"isInWorkflow\":false,\"isPdf\":false,\"isPdfUpToDate\":true,\"isWithdrawnOrRetracted\":false,\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"microbial-cell-factories\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":false,\"externalIdentity\":\"micf\",\"sideBox\":\"Learn more about [Microbial Cell Factories](http://microbialcellfactories.biomedcentral.com/)\",\"snPcode\":\"12934\",\"submissionUrl\":\"https://submission.nature.com/new-submission/12934/3\",\"title\":\"Microbial Cell Factories\",\"twitterHandle\":\"@BioMedCentral\",\"acdcEnabled\":true,\"dfaEnabled\":true,\"editorialSystem\":\"em\",\"reportingPortfolio\":\"BMC/SO AJ\",\"inReviewEnabled\":true,\"inReviewRevisionsEnabled\":true},\"keywords\":\"Limosilactobacillus reuteri, Metabolic Engineering, Pathway Optimization, 3-Hydroxypropionaldehyde, Carbon source utilization\",\"lastPublishedDoi\":\"10.21203/rs.3.rs-9269523/v1\",\"lastPublishedDoiUrl\":\"https://doi.org/10.21203/rs.3.rs-9269523/v1\",\"license\":{\"name\":\"CC BY 4.0\",\"url\":\"https://creativecommons.org/licenses/by/4.0/\"},\"manuscriptAbstract\":\"\\u003ch2\\u003eBackground\\u003c/h2\\u003e \\u003cp\\u003e \\u003cem\\u003eLimosilactobacillus reuteri\\u003c/em\\u003e is an important probiotic chassis for producing 3-hydroxypropionaldehyde (3-HPA), a broad-spectrum antimicrobial and biochemical. However, its production is constrained by a metabolic trilemma comprising coenzyme B₁₂ auxotrophy, redox imbalance, and carbon catabolite repression (CCR). The main bottleneck currently limiting cost-effective glucose-based production is the inability to simultaneously resolve these interconnected constraints.\\u003c/p\\u003e\\u003ch2\\u003eResults\\u003c/h2\\u003e \\u003cp\\u003eThrough integrated multi-omics analyses of differential carbon source performance, an unexpected regulatory mechanism was obtained. Galactose orchestrates a tripartite metabolic program wherein UDP-galactose acts as a central signaling molecule that simultaneously alleviates CCR via Crc inhibition, redirects carbon flux through the pentose phosphate pathway to balance ATP/NADH, and transcriptionally co-activates \\u003cem\\u003edhaB\\u003c/em\\u003e (glycerol dehydratase) and \\u003cem\\u003ecobA/Q\\u003c/em\\u003e (B₁₂ biosynthesis) for coordinated holoenzyme assembly. Strikingly, this native regulatory network enables 8.51 g/L 3-HPA from galactose despite its poor support for cell growth, versus 1.67 g/L from glucose which readily supports robust growth\\u0026mdash;a 5.1-fold advantage that highlights galactose's metabolic priming efficacy. Guided by these insights, an engineered strain overexpressing glycerol dehydratase was combined with process optimization and a rationally designed four-factor targeted supplementation. Synergistic supplementation with coenzyme B₁₂, ATP, KCl, and MgCl₂ achieved 28.96 g/L 3-HPA\\u0026mdash;a 17.34-fold improvement over native glucose-based performance.\\u003c/p\\u003e\\u003ch2\\u003eConclusions\\u003c/h2\\u003e \\u003cp\\u003eTo our knowledge, this is the first systems-level strategy for compensating the metabolic trilemma in \\u003cem\\u003eL. reuteri\\u003c/em\\u003e by engineering carbon source-dependent regulatory networks and cofactor dynamics in a cost-effective glucose-based system, achieving the highest 3-HPA titer reported in lactobacilli in shake-flask fermentation to date. This work establishes a scalable platform for industrial 3-HPA production and provides a systems-level framework for reprogramming central carbon metabolism in probiotic chassis to address persistent bioproduction bottlenecks.\\u003c/p\\u003e\",\"manuscriptTitle\":\"Multi-omics-guided metabolic engineering of Limosilactobacillus reuteri for high-level 3-hydroxypropionaldehyde production from glucose\",\"msid\":\"\",\"msnumber\":\"\",\"nonDraftVersions\":[{\"code\":1,\"date\":\"2026-04-06 08:27:34\",\"doi\":\"10.21203/rs.3.rs-9269523/v1\",\"editorialEvents\":[{\"type\":\"communityComments\",\"content\":0},{\"type\":\"decision\",\"content\":\"Revision requested\",\"date\":\"2026-05-08T07:07:14+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"editorInvitedReview\",\"content\":\"\",\"date\":\"2026-04-17T09:42:43+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"editorInvitedReview\",\"content\":\"\",\"date\":\"2026-04-10T13:32:21+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewerAgreed\",\"content\":\"168065169265485524127042250618178921749\",\"date\":\"2026-04-03T07:45:28+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewerAgreed\",\"content\":\"183835838874025623547410559447072929487\",\"date\":\"2026-04-01T09:15:50+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewersInvited\",\"content\":\"\",\"date\":\"2026-04-01T06:07:45+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"editorAssigned\",\"content\":\"\",\"date\":\"2026-03-31T17:05:46+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"checksComplete\",\"content\":\"\",\"date\":\"2026-03-31T16:19:17+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"submitted\",\"content\":\"Microbial Cell Factories\",\"date\":\"2026-03-30T15:52:49+00:00\",\"index\":\"\",\"fulltext\":\"\"}],\"status\":\"published\",\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"microbial-cell-factories\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":false,\"externalIdentity\":\"micf\",\"sideBox\":\"Learn more about [Microbial Cell Factories](http://microbialcellfactories.biomedcentral.com/)\",\"snPcode\":\"12934\",\"submissionUrl\":\"https://submission.nature.com/new-submission/12934/3\",\"title\":\"Microbial Cell Factories\",\"twitterHandle\":\"@BioMedCentral\",\"acdcEnabled\":true,\"dfaEnabled\":true,\"editorialSystem\":\"em\",\"reportingPortfolio\":\"BMC/SO AJ\",\"inReviewEnabled\":true,\"inReviewRevisionsEnabled\":true}}],\"origin\":\"\",\"ownerIdentity\":\"0038e848-2b67-4dc2-ae8c-b31a9fa3a882\",\"owner\":[],\"postedDate\":\"April 6th, 2026\",\"published\":true,\"recentEditorialEvents\":[{\"type\":\"decision\",\"content\":\"Revision requested\",\"date\":\"2026-05-08T07:07:14+00:00\",\"index\":\"\",\"fulltext\":\"\"}],\"rejectedJournal\":[],\"revision\":\"\",\"amendment\":\"\",\"status\":\"in-revision\",\"subjectAreas\":[],\"tags\":[],\"updatedAt\":\"2026-05-08T07:23:09+00:00\",\"versionOfRecord\":[],\"versionCreatedAt\":\"2026-04-06 08:27:34\",\"video\":\"\",\"vorDoi\":\"\",\"vorDoiUrl\":\"\",\"workflowStages\":[]},\"version\":\"v1\",\"identity\":\"rs-9269523\",\"journalConfig\":\"researchsquare\"},\"__N_SSP\":true},\"page\":\"/article/[identity]/[[...version]]\",\"query\":{\"redirect\":\"/article/rs-9269523\",\"identity\":\"rs-9269523\",\"version\":[\"v1\"]},\"buildId\":\"XKTyCvWXoU3ODBz1xrDgd\",\"isFallback\":false,\"isExperimentalCompile\":false,\"dynamicIds\":[84888],\"gssp\":true,\"scriptLoader\":[]}","source_license":"CC-BY-4.0","license_restricted":false}